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Large-Capacity and Flexible Video Steganography via Invertible Neural Network | Chong Mou, Youmin Xu, Jiechong Song, Chen Zhao, Bernard Ghanem, Jian Zhang | Video steganography is the art of unobtrusively concealing secret data in a cover video and then recovering the secret data through a decoding protocol at the receiver end. Although several attempts have been made, most of them are limited to low-capacity and fixed steganography. To rectify these weaknesses, we propose a Large-capacity and Flexible Video Steganography Network (LF-VSN) in this paper. For large-capacity, we present a reversible pipeline to perform multiple videos hiding and recovering through a single invertible neural network (INN). Our method can hide/recover 7 secret videos in/from 1 cover video with promising performance. For flexibility, we propose a key-controllable scheme, enabling different receivers to recover particular secret videos from the same cover video through specific keys. Moreover, we further improve the flexibility by proposing a scalable strategy in multiple videos hiding, which can hide variable numbers of secret videos in a cover video with a single model and a single training session. Extensive experiments demonstrate that with the significant improvement of the video steganography performance, our proposed LF-VSN has high security, large hiding capacity, and flexibility. The source code is available at https://github.com/MC-E/LF-VSN. | https://openaccess.thecvf.com/content/CVPR2023/papers/Mou_Large-Capacity_and_Flexible_Video_Steganography_via_Invertible_Neural_Network_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2304.12300 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Mou_Large-Capacity_and_Flexible_Video_Steganography_via_Invertible_Neural_Network_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Mou_Large-Capacity_and_Flexible_Video_Steganography_via_Invertible_Neural_Network_CVPR_2023_paper.html | CVPR 2023 | null |
CFA: Class-Wise Calibrated Fair Adversarial Training | Zeming Wei, Yifei Wang, Yiwen Guo, Yisen Wang | Adversarial training has been widely acknowledged as the most effective method to improve the adversarial robustness against adversarial examples for Deep Neural Networks (DNNs). So far, most existing works focus on enhancing the overall model robustness, treating each class equally in both the training and testing phases. Although revealing the disparity in robustness among classes, few works try to make adversarial training fair at the class level without sacrificing overall robustness. In this paper, we are the first to theoretically and empirically investigate the preference of different classes for adversarial configurations, including perturbation margin, regularization, and weight averaging. Motivated by this, we further propose a Class-wise calibrated Fair Adversarial training framework, named CFA, which customizes specific training configurations for each class automatically. Experiments on benchmark datasets demonstrate that our proposed CFA can improve both overall robustness and fairness notably over other state-of-the-art methods. Code is available at https://github.com/PKU-ML/CFA. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wei_CFA_Class-Wise_Calibrated_Fair_Adversarial_Training_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wei_CFA_Class-Wise_Calibrated_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14460 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_CFA_Class-Wise_Calibrated_Fair_Adversarial_Training_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_CFA_Class-Wise_Calibrated_Fair_Adversarial_Training_CVPR_2023_paper.html | CVPR 2023 | null |
EVAL: Explainable Video Anomaly Localization | Ashish Singh, Michael J. Jones, Erik G. Learned-Miller | We develop a novel framework for single-scene video anomaly localization that allows for human-understandable reasons for the decisions the system makes. We first learn general representations of objects and their motions (using deep networks) and then use these representations to build a high-level, location-dependent model of any particular scene. This model can be used to detect anomalies in new videos of the same scene. Importantly, our approach is explainable -- our high-level appearance and motion features can provide human-understandable reasons for why any part of a video is classified as normal or anomalous. We conduct experiments on standard video anomaly detection datasets (Street Scene, CUHK Avenue, ShanghaiTech and UCSD Ped1, Ped2) and show significant improvements over the previous state-of-the-art. All of our code and extra datasets will be made publicly available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Singh_EVAL_Explainable_Video_Anomaly_Localization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Singh_EVAL_Explainable_Video_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2212.07900 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Singh_EVAL_Explainable_Video_Anomaly_Localization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Singh_EVAL_Explainable_Video_Anomaly_Localization_CVPR_2023_paper.html | CVPR 2023 | null |
Position-Guided Text Prompt for Vision-Language Pre-Training | Jinpeng Wang, Pan Zhou, Mike Zheng Shou, Shuicheng Yan | Vision-Language Pre-Training (VLP) has shown promising capabilities to align image and text pairs, facilitating a broad variety of cross-modal learning tasks. However, we observe that VLP models often lack the visual grounding/localization capability which is critical for many downstream tasks such as visual reasoning. In this work, we propose a novel Position-guided Text Prompt (PTP) paradigm to enhance the visual grounding ability of cross-modal models trained with VLP. Specifically, in the VLP phase, PTP divides the image into NxN blocks, and identifies the objects in each block through the widely used object detector in VLP. It then reformulates the visual grounding task into a fill-in-the-blank problem given a PTP by encouraging the model to predict the objects in the given blocks or regress the blocks of a given object, e.g. filling "P" or "O" in a PTP "The block P has a O". This mechanism improves the visual grounding capability of VLP models and thus helps them better handle various downstream tasks. By introducing PTP into several state-of-the-art VLP frameworks, we observe consistently significant improvements across representative cross-modal learning model architectures and several benchmarks, e.g. zero-shot Flickr30K Retrieval (+4.8 in average recall@1) for ViLT baseline, and COCO Captioning (+5.3 in CIDEr) for SOTA BLIP baseline. Moreover, PTP achieves comparable results with object-detector based methods, and much faster inference speed since PTP discards its object detector for inference while the later cannot. Our code and pre-trained weight will be released. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Position-Guided_Text_Prompt_for_Vision-Language_Pre-Training_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2212.09737 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Position-Guided_Text_Prompt_for_Vision-Language_Pre-Training_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Position-Guided_Text_Prompt_for_Vision-Language_Pre-Training_CVPR_2023_paper.html | CVPR 2023 | null |
HOLODIFFUSION: Training a 3D Diffusion Model Using 2D Images | Animesh Karnewar, Andrea Vedaldi, David Novotny, Niloy J. Mitra | Diffusion models have emerged as the best approach for generative modeling of 2D images. Part of their success is due to the possibility of training them on millions if not billions of images with a stable learning objective. However, extending these models to 3D remains difficult for two reasons. First, finding a large quantity of 3D training data is much more complex than for 2D images. Second, while it is conceptually trivial to extend the models to operate on 3D rather than 2D grids, the associated cubic growth in memory and compute complexity makes this infeasible. We address the first challenge by introducing a new diffusion setup that can be trained, end-to-end, with only posed 2D images for supervision; and the second challenge by proposing an image formation model that decouples model memory from spatial memory. We evaluate our method on real-world data, using the CO3D dataset which has not been used to train 3D generative models before. We show that our diffusion models are scalable, train robustly, and are competitive in terms of sample quality and fidelity to existing approaches for 3D generative modeling. | https://openaccess.thecvf.com/content/CVPR2023/papers/Karnewar_HOLODIFFUSION_Training_a_3D_Diffusion_Model_Using_2D_Images_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Karnewar_HOLODIFFUSION_Training_a_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16509 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Karnewar_HOLODIFFUSION_Training_a_3D_Diffusion_Model_Using_2D_Images_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Karnewar_HOLODIFFUSION_Training_a_3D_Diffusion_Model_Using_2D_Images_CVPR_2023_paper.html | CVPR 2023 | null |
Stimulus Verification Is a Universal and Effective Sampler in Multi-Modal Human Trajectory Prediction | Jianhua Sun, Yuxuan Li, Liang Chai, Cewu Lu | To comprehensively cover the uncertainty of the future, the common practice of multi-modal human trajectory prediction is to first generate a set/distribution of candidate future trajectories and then sample required numbers of trajectories from them as final predictions. Even though a large number of previous researches develop various strong models to predict candidate trajectories, how to effectively sample the final ones has not received much attention yet. In this paper, we propose stimulus verification, serving as a universal and effective sampling process to improve the multi-modal prediction capability, where stimulus refers to the factor in the observation that may affect the future movements such as social interaction and scene context. Stimulus verification introduces a probabilistic model, denoted as stimulus verifier, to verify the coherence between a predicted future trajectory and its corresponding stimulus. By highlighting prediction samples with better stimulus-coherence, stimulus verification ensures sampled trajectories plausible from the stimulus' point of view and therefore aids in better multi-modal prediction performance. We implement stimulus verification on five representative prediction frameworks and conduct exhaustive experiments on three widely-used benchmarks. Superior results demonstrate the effectiveness of our approach. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sun_Stimulus_Verification_Is_a_Universal_and_Effective_Sampler_in_Multi-Modal_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sun_Stimulus_Verification_Is_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Stimulus_Verification_Is_a_Universal_and_Effective_Sampler_in_Multi-Modal_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Stimulus_Verification_Is_a_Universal_and_Effective_Sampler_in_Multi-Modal_CVPR_2023_paper.html | CVPR 2023 | null |
3D Human Pose Estimation With Spatio-Temporal Criss-Cross Attention | Zhenhua Tang, Zhaofan Qiu, Yanbin Hao, Richang Hong, Ting Yao | Recent transformer-based solutions have shown great success in 3D human pose estimation. Nevertheless, to calculate the joint-to-joint affinity matrix, the computational cost has a quadratic growth with the increasing number of joints. Such drawback becomes even worse especially for pose estimation in a video sequence, which necessitates spatio-temporal correlation spanning over the entire video. In this paper, we facilitate the issue by decomposing correlation learning into space and time, and present a novel Spatio-Temporal Criss-cross attention (STC) block. Technically, STC first slices its input feature into two partitions evenly along the channel dimension, followed by performing spatial and temporal attention respectively on each partition. STC then models the interactions between joints in an identical frame and joints in an identical trajectory simultaneously by concatenating the outputs from attention layers. On this basis, we devise STCFormer by stacking multiple STC blocks and further integrate a new Structure-enhanced Positional Embedding (SPE) into STCFormer to take the structure of human body into consideration. The embedding function consists of two components: spatio-temporal convolution around neighboring joints to capture local structure, and part-aware embedding to indicate which part each joint belongs to. Extensive experiments are conducted on Human3.6M and MPI-INF-3DHP benchmarks, and superior results are reported when comparing to the state-of-the-art approaches. More remarkably, STCFormer achieves to-date the best published performance: 40.5mm P1 error on the challenging Human3.6M dataset. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tang_3D_Human_Pose_Estimation_With_Spatio-Temporal_Criss-Cross_Attention_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tang_3D_Human_Pose_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_3D_Human_Pose_Estimation_With_Spatio-Temporal_Criss-Cross_Attention_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_3D_Human_Pose_Estimation_With_Spatio-Temporal_Criss-Cross_Attention_CVPR_2023_paper.html | CVPR 2023 | null |
Plateau-Reduced Differentiable Path Tracing | Michael Fischer, Tobias Ritschel | Current differentiable renderers provide light transport gradients with respect to arbitrary scene parameters. However, the mere existence of these gradients does not guarantee useful update steps in an optimization. Instead, inverse rendering might not converge due to inherent plateaus, i.e., regions of zero gradient, in the objective function. We propose to alleviate this by convolving the high-dimensional rendering function that maps scene parameters to images with an additional kernel that blurs the parameter space. We describe two Monte Carlo estimators to compute plateau-free gradients efficiently, i.e., with low variance, and show that these translate into net-gains in optimization error and runtime performance. Our approach is a straightforward extension to both black-box and differentiable renderers and enables the successful optimization of problems with intricate light transport, such as caustics or global illumination, that existing differentiable path tracers do not converge on. Our code is at github.com/mfischer-ucl/prdpt. | https://openaccess.thecvf.com/content/CVPR2023/papers/Fischer_Plateau-Reduced_Differentiable_Path_Tracing_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Fischer_Plateau-Reduced_Differentiable_Path_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.17263 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fischer_Plateau-Reduced_Differentiable_Path_Tracing_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fischer_Plateau-Reduced_Differentiable_Path_Tracing_CVPR_2023_paper.html | CVPR 2023 | null |
LoGoNet: Towards Accurate 3D Object Detection With Local-to-Global Cross-Modal Fusion | Xin Li, Tao Ma, Yuenan Hou, Botian Shi, Yuchen Yang, Youquan Liu, Xingjiao Wu, Qin Chen, Yikang Li, Yu Qiao, Liang He | LiDAR-camera fusion methods have shown impressive performance in 3D object detection. Recent advanced multi-modal methods mainly perform global fusion, where image features and point cloud features are fused across the whole scene. Such practice lacks fine-grained region-level information, yielding suboptimal fusion performance. In this paper, we present the novel Local-to-Global fusion network (LoGoNet), which performs LiDAR-camera fusion at both local and global levels. Concretely, the Global Fusion (GoF) of LoGoNet is built upon previous literature, while we exclusively use point centroids to more precisely represent the position of voxel features, thus achieving better cross-modal alignment. As to the Local Fusion (LoF), we first divide each proposal into uniform grids and then project these grid centers to the images. The image features around the projected grid points are sampled to be fused with position-decorated point cloud features, maximally utilizing the rich contextual information around the proposals. The Feature Dynamic Aggregation (FDA) module is further proposed to achieve information interaction between these locally and globally fused features, thus producing more informative multi-modal features. Extensive experiments on both Waymo Open Dataset (WOD) and KITTI datasets show that LoGoNet outperforms all state-of-the-art 3D detection methods. Notably, LoGoNet ranks 1st on Waymo 3D object detection leaderboard and obtains 81.02 mAPH (L2) detection performance. It is noteworthy that, for the first time, the detection performance on three classes surpasses 80 APH (L2) simultaneously. Code will be available at https://github.com/sankin97/LoGoNet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_LoGoNet_Towards_Accurate_3D_Object_Detection_With_Local-to-Global_Cross-Modal_Fusion_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_LoGoNet_Towards_Accurate_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.03595 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_LoGoNet_Towards_Accurate_3D_Object_Detection_With_Local-to-Global_Cross-Modal_Fusion_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_LoGoNet_Towards_Accurate_3D_Object_Detection_With_Local-to-Global_Cross-Modal_Fusion_CVPR_2023_paper.html | CVPR 2023 | null |
ScaleKD: Distilling Scale-Aware Knowledge in Small Object Detector | Yichen Zhu, Qiqi Zhou, Ning Liu, Zhiyuan Xu, Zhicai Ou, Xiaofeng Mou, Jian Tang | Despite the prominent success of general object detection, the performance and efficiency of Small Object Detection (SOD) are still unsatisfactory. Unlike existing works that struggle to balance the trade-off between inference speed and SOD performance, in this paper, we propose a novel Scale-aware Knowledge Distillation (ScaleKD), which transfers knowledge of a complex teacher model to a compact student model. We design two novel modules to boost the quality of knowledge transfer in distillation for SOD: 1) a scale-decoupled feature distillation module that disentangled teacher's feature representation into multi-scale embedding that enables explicit feature mimicking of the student model on small objects. 2) a cross-scale assistant to refine the noisy and uninformative bounding boxes prediction student models, which can mislead the student model and impair the efficacy of knowledge distillation. A multi-scale cross-attention layer is established to capture the multi-scale semantic information to improve the student model. We conduct experiments on COCO and VisDrone datasets with diverse types of models, i.e., two-stage and one-stage detectors, to evaluate our proposed method. Our ScaleKD achieves superior performance on general detection performance and obtains spectacular improvement regarding the SOD performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_ScaleKD_Distilling_Scale-Aware_Knowledge_in_Small_Object_Detector_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_ScaleKD_Distilling_Scale-Aware_Knowledge_in_Small_Object_Detector_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_ScaleKD_Distilling_Scale-Aware_Knowledge_in_Small_Object_Detector_CVPR_2023_paper.html | CVPR 2023 | null |
An Empirical Study of End-to-End Video-Language Transformers With Masked Visual Modeling | Tsu-Jui Fu, Linjie Li, Zhe Gan, Kevin Lin, William Yang Wang, Lijuan Wang, Zicheng Liu | Masked visual modeling (MVM) has been recently proven effective for visual pre-training. While similar reconstructive objectives on video inputs (e.g., masked frame modeling) have been explored in video-language (VidL) pre-training, previous studies fail to find a truly effective MVM strategy that can largely benefit the downstream performance. In this work, we systematically examine the potential of MVM in the context of VidL learning. Specifically, we base our study on a fully end-to-end VIdeO-LanguagE Transformer (VIOLET), where the supervision from MVM training can be backpropagated to the video pixel space. In total, eight different reconstructive targets of MVM are explored, from low-level pixel values and oriented gradients to high-level depth maps, optical flow, discrete visual tokens, and latent visual features. We conduct comprehensive experiments and provide insights into the factors leading to effective MVM training, resulting in an enhanced model VIOLETv2. Empirically, we show VIOLETv2 pre-trained with MVM objective achieves notable improvements on 13 VidL benchmarks, ranging from video question answering, video captioning, to text-to-video retrieval. | https://openaccess.thecvf.com/content/CVPR2023/papers/Fu_An_Empirical_Study_of_End-to-End_Video-Language_Transformers_With_Masked_Visual_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Fu_An_Empirical_Study_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2209.01540 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fu_An_Empirical_Study_of_End-to-End_Video-Language_Transformers_With_Masked_Visual_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fu_An_Empirical_Study_of_End-to-End_Video-Language_Transformers_With_Masked_Visual_CVPR_2023_paper.html | CVPR 2023 | null |
Glocal Energy-Based Learning for Few-Shot Open-Set Recognition | Haoyu Wang, Guansong Pang, Peng Wang, Lei Zhang, Wei Wei, Yanning Zhang | Few-shot open-set recognition (FSOR) is a challenging task of great practical value. It aims to categorize a sample to one of the pre-defined, closed-set classes illustrated by few examples while being able to reject the sample from unknown classes. In this work, we approach the FSOR task by proposing a novel energy-based hybrid model. The model is composed of two branches, where a classification branch learns a metric to classify a sample to one of closed-set classes and the energy branch explicitly estimates the open-set probability. To achieve holistic detection of open-set samples, our model leverages both class-wise and pixel-wise features to learn a glocal energy-based score, in which a global energy score is learned using the class-wise features, while a local energy score is learned using the pixel-wise features. The model is enforced to assign large energy scores to samples that are deviated from the few-shot examples in either the class-wise features or the pixel-wise features, and to assign small energy scores otherwise. Experiments on three standard FSOR datasets show the superior performance of our model. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Glocal_Energy-Based_Learning_for_Few-Shot_Open-Set_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Glocal_Energy-Based_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.11855 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Glocal_Energy-Based_Learning_for_Few-Shot_Open-Set_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Glocal_Energy-Based_Learning_for_Few-Shot_Open-Set_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
Revisiting Temporal Modeling for CLIP-Based Image-to-Video Knowledge Transferring | Ruyang Liu, Jingjia Huang, Ge Li, Jiashi Feng, Xinglong Wu, Thomas H. Li | Image-text pretrained models, e.g., CLIP, have shown impressive general multi-modal knowledge learned from large-scale image-text data pairs, thus attracting increasing attention for their potential to improve visual representation learning in the video domain. In this paper, based on the CLIP model, we revisit temporal modeling in the context of image-to-video knowledge transferring, which is the key point for extending image-text pretrained models to the video domain. We find that current temporal modeling mechanisms are tailored to either high-level semantic-dominant tasks (e.g., retrieval) or low-level visual pattern-dominant tasks (e.g., recognition), and fail to work on the two cases simultaneously. The key difficulty lies in modeling temporal dependency while taking advantage of both high-level and low-level knowledge in CLIP model. To tackle this problem, we present Spatial-Temporal Auxiliary Network (STAN) -- a simple and effective temporal modeling mechanism extending CLIP model to diverse video tasks. Specifically, to realize both low-level and high-level knowledge transferring, STAN adopts a branch structure with decomposed spatial-temporal modules that enable multi-level CLIP features to be spatial-temporally contextualized. We evaluate our method on two representative video tasks: Video-Text Retrieval and Video Recognition. Extensive experiments demonstrate the superiority of our model over the state-of-the-art methods on various datasets, including MSR-VTT, DiDeMo, LSMDC, MSVD, Kinetics-400, and Something-Something-V2. Codes will be available at https://github.com/farewellthree/STAN | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Revisiting_Temporal_Modeling_for_CLIP-Based_Image-to-Video_Knowledge_Transferring_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2301.11116 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Revisiting_Temporal_Modeling_for_CLIP-Based_Image-to-Video_Knowledge_Transferring_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Revisiting_Temporal_Modeling_for_CLIP-Based_Image-to-Video_Knowledge_Transferring_CVPR_2023_paper.html | CVPR 2023 | null |
MethaneMapper: Spectral Absorption Aware Hyperspectral Transformer for Methane Detection | Satish Kumar, Ivan Arevalo, ASM Iftekhar, B S Manjunath | Methane (CH 4 ) is the chief contributor to global climate change. Recent Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) has been very useful in quantitative mapping of methane emissions. Existing methods for analyzing this data are sensitive to local terrain conditions, often require manual inspection from domain experts, prone to significant error and hence are not scalable. To address these challenges, we propose a novel end-to-end spectral absorption wavelength aware transformer network, MethaneMapper, to detect and quantify the emissions. MethaneMapper introduces two novel modules that help to locate the most relevant methane plume regions in the spectral domain and uses them to localize these accurately. Thorough evaluation shows that MethaneMapper achieves 0.63 mAP in detection and reduces the model size (by 5x) compared to the current state of the art. In addition, we also introduce a large-scale dataset of methane plume segmentation mask for over 1200 AVIRIS-NG flightlines from 2015-2022. It contains over 4000 methane plume sites. Our dataset will provide researchers the opportunity to develop and advance new methods for tackling this challenging green-house gas detection problem with significant broader social impact. Dataset and source code link. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kumar_MethaneMapper_Spectral_Absorption_Aware_Hyperspectral_Transformer_for_Methane_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kumar_MethaneMapper_Spectral_Absorption_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.02767 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kumar_MethaneMapper_Spectral_Absorption_Aware_Hyperspectral_Transformer_for_Methane_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kumar_MethaneMapper_Spectral_Absorption_Aware_Hyperspectral_Transformer_for_Methane_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Autonomous Manipulation Learning for Similar Deformable Objects via Only One Demonstration | Yu Ren, Ronghan Chen, Yang Cong | In comparison with most methods focusing on 3D rigid object recognition and manipulation, deformable objects are more common in our real life but attract less attention. Generally, most existing methods for deformable object manipulation suffer two issues, 1) Massive demonstration: repeating thousands of robot-object demonstrations for model training of one specific instance; 2) Poor generalization: inevitably re-training for transferring the learned skill to a similar/new instance from the same category. Therefore, we propose a category-level deformable 3D object manipulation framework, which could manipulate deformable 3D objects with only one demonstration and generalize the learned skills to new similar instances without re-training. Specifically, our proposed framework consists of two modules. The Nocs State Transform (NST) module transfers the observed point clouds of the target to a pre-defined unified pose state (i.e., Nocs state), which is the foundation for the category-level manipulation learning; the Neural Spatial Encoding (NSE) module generalizes the learned skill to novel instances by encoding the category-level spatial information to pursue the expected grasping point without re-training. The relative motion path is then planned to achieve autonomous manipulation. Both the simulated results via our Cap40 dataset and real robotic experiments justify the effectiveness of our framework. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ren_Autonomous_Manipulation_Learning_for_Similar_Deformable_Objects_via_Only_One_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ren_Autonomous_Manipulation_Learning_for_Similar_Deformable_Objects_via_Only_One_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ren_Autonomous_Manipulation_Learning_for_Similar_Deformable_Objects_via_Only_One_CVPR_2023_paper.html | CVPR 2023 | null |
Representation Learning for Visual Object Tracking by Masked Appearance Transfer | Haojie Zhao, Dong Wang, Huchuan Lu | Visual representation plays an important role in visual object tracking. However, few works study the tracking-specified representation learning method. Most trackers directly use ImageNet pre-trained representations. In this paper, we propose masked appearance transfer, a simple but effective representation learning method for tracking, based on an encoder-decoder architecture. First, we encode the visual appearances of the template and search region jointly, and then we decode them separately. During decoding, the original search region image is reconstructed. However, for the template, we make the decoder reconstruct the target appearance within the search region. By this target appearance transfer, the tracking-specified representations are learned. We randomly mask out the inputs, thereby making the learned representations more discriminative. For sufficient evaluation, we design a simple and lightweight tracker that can evaluate the representation for both target localization and box regression. Extensive experiments show that the proposed method is effective, and the learned representations can enable the simple tracker to obtain state-of-the-art performance on six datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhao_Representation_Learning_for_Visual_Object_Tracking_by_Masked_Appearance_Transfer_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhao_Representation_Learning_for_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Representation_Learning_for_Visual_Object_Tracking_by_Masked_Appearance_Transfer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Representation_Learning_for_Visual_Object_Tracking_by_Masked_Appearance_Transfer_CVPR_2023_paper.html | CVPR 2023 | null |
EFEM: Equivariant Neural Field Expectation Maximization for 3D Object Segmentation Without Scene Supervision | Jiahui Lei, Congyue Deng, Karl Schmeckpeper, Leonidas Guibas, Kostas Daniilidis | We introduce Equivariant Neural Field Expectation Maximization (EFEM), a simple, effective, and robust geometric algorithm that can segment objects in 3D scenes without annotations or training on scenes. We achieve such unsupervised segmentation by exploiting single object shape priors. We make two novel steps in that direction. First, we introduce equivariant shape representations to this problem to eliminate the complexity induced by the variation in object configuration. Second, we propose a novel EM algorithm that can iteratively refine segmentation masks using the equivariant shape prior. We collect a novel real dataset Chairs and Mugs that contains various object configurations and novel scenes in order to verify the effectiveness and robustness of our method. Experimental results demonstrate that our method achieves consistent and robust performance across different scenes where the (weakly) supervised methods may fail. Code and data available at https://www.cis.upenn.edu/ leijh/projects/efem | https://openaccess.thecvf.com/content/CVPR2023/papers/Lei_EFEM_Equivariant_Neural_Field_Expectation_Maximization_for_3D_Object_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lei_EFEM_Equivariant_Neural_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15440 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lei_EFEM_Equivariant_Neural_Field_Expectation_Maximization_for_3D_Object_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lei_EFEM_Equivariant_Neural_Field_Expectation_Maximization_for_3D_Object_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Learning To Name Classes for Vision and Language Models | Sarah Parisot, Yongxin Yang, Steven McDonagh | Large scale vision and language models can achieve impressive zero-shot recognition performance by mapping class specific text queries to image content. Two distinct challenges that remain however, are high sensitivity to the choice of handcrafted class names that define queries, and the difficulty of adaptation to new, smaller datasets. Towards addressing these problems, we propose to leverage available data to learn, for each class, an optimal word embedding as a function of the visual content. By learning new word embeddings on an otherwise frozen model, we are able to retain zero-shot capabilities for new classes, easily adapt models to new datasets, and adjust potentially erroneous, non-descriptive or ambiguous class names. We show that our solution can easily be integrated in image classification and object detection pipelines, yields significant performance gains in multiple scenarios and provides insights into model biases and labelling errors. | https://openaccess.thecvf.com/content/CVPR2023/papers/Parisot_Learning_To_Name_Classes_for_Vision_and_Language_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Parisot_Learning_To_Name_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.01830 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Parisot_Learning_To_Name_Classes_for_Vision_and_Language_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Parisot_Learning_To_Name_Classes_for_Vision_and_Language_Models_CVPR_2023_paper.html | CVPR 2023 | null |
ECON: Explicit Clothed Humans Optimized via Normal Integration | Yuliang Xiu, Jinlong Yang, Xu Cao, Dimitrios Tzionas, Michael J. Black | The combination of deep learning, artist-curated scans, and Implicit Functions (IF), is enabling the creation of detailed, clothed, 3D humans from images. However, existing methods are far from perfect. IF-based methods recover free-form geometry, but produce disembodied limbs or degenerate shapes for novel poses or clothes. To increase robustness for these cases, existing work uses an explicit parametric body model to constrain surface reconstruction, but this limits the recovery of free-form surfaces such as loose clothing that deviates from the body. What we want is a method that combines the best properties of implicit representation and explicit body regularization. To this end, we make two key observations: (1) current networks are better at inferring detailed 2D maps than full-3D surfaces, and (2) a parametric model can be seen as a "canvas" for stitching together detailed surface patches. Based on these, our method, ECON, has three main steps: (1) It infers detailed 2D normal maps for the front and back side of a clothed person. (2) From these, it recovers 2.5D front and back surfaces, called d-BiNI, that are equally detailed, yet incomplete, and registers these w.r.t. each other with the help of a SMPL-X body mesh recovered from the image. (3) It "inpaints" the missing geometry between d-BiNI surfaces. If the face and hands are noisy, they can optionally be replaced with the ones of SMPL-X. As a result, ECON infers high-fidelity 3D humans even in loose clothes and challenging poses. This goes beyond previous methods, according to the quantitative evaluation on the CAPE and Renderpeople datasets. Perceptual studies also show that ECON's perceived realism is better by a large margin. Code and models are available for research purposes at econ.is.tue.mpg.de | https://openaccess.thecvf.com/content/CVPR2023/papers/Xiu_ECON_Explicit_Clothed_Humans_Optimized_via_Normal_Integration_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xiu_ECON_Explicit_Clothed_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.07422 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xiu_ECON_Explicit_Clothed_Humans_Optimized_via_Normal_Integration_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xiu_ECON_Explicit_Clothed_Humans_Optimized_via_Normal_Integration_CVPR_2023_paper.html | CVPR 2023 | null |
Neural Fourier Filter Bank | Zhijie Wu, Yuhe Jin, Kwang Moo Yi | We present a novel method to provide efficient and highly detailed reconstructions. Inspired by wavelets, we learn a neural field that decompose the signal both spatially and frequency-wise. We follow the recent grid-based paradigm for spatial decomposition, but unlike existing work, encourage specific frequencies to be stored in each grid via Fourier features encodings. We then apply a multi-layer perceptron with sine activations, taking these Fourier encoded features in at appropriate layers so that higher-frequency components are accumulated on top of lower-frequency components sequentially, which we sum up to form the final output. We demonstrate that our method outperforms the state of the art regarding model compactness and convergence speed on multiple tasks: 2D image fitting, 3D shape reconstruction, and neural radiance fields. Our code is available at https://github.com/ubc-vision/NFFB. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_Neural_Fourier_Filter_Bank_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_Neural_Fourier_Filter_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.01735 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Neural_Fourier_Filter_Bank_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Neural_Fourier_Filter_Bank_CVPR_2023_paper.html | CVPR 2023 | null |
F2-NeRF: Fast Neural Radiance Field Training With Free Camera Trajectories | Peng Wang, Yuan Liu, Zhaoxi Chen, Lingjie Liu, Ziwei Liu, Taku Komura, Christian Theobalt, Wenping Wang | This paper presents a novel grid-based NeRF called F^2-NeRF (Fast-Free-NeRF) for novel view synthesis, which enables arbitrary input camera trajectories and only costs a few minutes for training. Existing fast grid-based NeRF training frameworks, like Instant-NGP, Plenoxels, DVGO, or TensoRF, are mainly designed for bounded scenes and rely on space warping to handle unbounded scenes. Existing two widely-used space-warping methods are only designed for the forward-facing trajectory or the 360deg object-centric trajectory but cannot process arbitrary trajectories. In this paper, we delve deep into the mechanism of space warping to handle unbounded scenes. Based on our analysis, we further propose a novel space-warping method called perspective warping, which allows us to handle arbitrary trajectories in the grid-based NeRF framework. Extensive experiments demonstrate that F^2-NeRF is able to use the same perspective warping to render high-quality images on two standard datasets and a new free trajectory dataset collected by us. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_F2-NeRF_Fast_Neural_Radiance_Field_Training_With_Free_Camera_Trajectories_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_F2-NeRF_Fast_Neural_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_F2-NeRF_Fast_Neural_Radiance_Field_Training_With_Free_Camera_Trajectories_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_F2-NeRF_Fast_Neural_Radiance_Field_Training_With_Free_Camera_Trajectories_CVPR_2023_paper.html | CVPR 2023 | null |
NeRFInvertor: High Fidelity NeRF-GAN Inversion for Single-Shot Real Image Animation | Yu Yin, Kamran Ghasedi, HsiangTao Wu, Jiaolong Yang, Xin Tong, Yun Fu | Nerf-based Generative models have shown impressive capacity in generating high-quality images with consistent 3D geometry. Despite successful synthesis of fake identity images randomly sampled from latent space, adopting these models for generating face images of real subjects is still a challenging task due to its so-called inversion issue. In this paper, we propose a universal method to surgically fine-tune these NeRF-GAN models in order to achieve high-fidelity animation of real subjects only by a single image. Given the optimized latent code for an out-of-domain real image, we employ 2D loss functions on the rendered image to reduce the identity gap. Furthermore, our method leverages explicit and implicit 3D regularizations using the in-domain neighborhood samples around the optimized latent code to remove geometrical and visual artifacts. Our experiments confirm the effectiveness of our method in realistic, high-fidelity, and 3D consistent animation of real faces on multiple NeRF-GAN models across different datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yin_NeRFInvertor_High_Fidelity_NeRF-GAN_Inversion_for_Single-Shot_Real_Image_Animation_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2211.17235 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yin_NeRFInvertor_High_Fidelity_NeRF-GAN_Inversion_for_Single-Shot_Real_Image_Animation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yin_NeRFInvertor_High_Fidelity_NeRF-GAN_Inversion_for_Single-Shot_Real_Image_Animation_CVPR_2023_paper.html | CVPR 2023 | null |
Learning To Detect and Segment for Open Vocabulary Object Detection | Tao Wang | Open vocabulary object detection has been greately advanced by the recent development of vision-language pre-trained model, which helps recognizing the novel objects with only semantic categories. The prior works mainly focus on knowledge transferring to the object proposal classification and employ class-agnostic box and mask prediction. In this work, we propose CondHead, a principled dynamic network design to better generalize the box regression and mask segmentation for open vocabulary setting. The core idea is to conditionally parametrize the network heads on semantic embedding and thus the model is guided with class-specific knowledge to better detect novel categories. Specifically, CondHead is composed of two streams of network heads, the dynamically aggregated heads and dynamically generated heads. The former is instantiated with a set of static heads that are conditionally aggregated, these heads are optimized as experts and are expected to learn sophisticated prediction. The Latter is instantiated with dynamically generated parameters and encodes general class-specific information. With such conditional design, the detection model is bridged by the semantic embedding to offer strongly generalizable class-wise box and mask prediction. Our method brings significant improvement to the prior state-of-the-art open vocabulary object detection methods with very minor overhead, e.g., it surpasses a RegionClip model by 3.0 detection AP on novel categories, with only 1.1% more computation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Learning_To_Detect_and_Segment_for_Open_Vocabulary_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Learning_To_Detect_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.12130 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Learning_To_Detect_and_Segment_for_Open_Vocabulary_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Learning_To_Detect_and_Segment_for_Open_Vocabulary_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Disentangling Writer and Character Styles for Handwriting Generation | Gang Dai, Yifan Zhang, Qingfeng Wang, Qing Du, Zhuliang Yu, Zhuoman Liu, Shuangping Huang | Training machines to synthesize diverse handwritings is an intriguing task. Recently, RNN-based methods have been proposed to generate stylized online Chinese characters. However, these methods mainly focus on capturing a person's overall writing style, neglecting subtle style inconsistencies between characters written by the same person. For example, while a person's handwriting typically exhibits general uniformity (e.g., glyph slant and aspect ratios), there are still small style variations in finer details (e.g., stroke length and curvature) of characters. In light of this, we propose to disentangle the style representations at both writer and character levels from individual handwritings to synthesize realistic stylized online handwritten characters. Specifically, we present the style-disentangled Transformer (SDT), which employs two complementary contrastive objectives to extract the style commonalities of reference samples and capture the detailed style patterns of each sample, respectively. Extensive experiments on various language scripts demonstrate the effectiveness of SDT. Notably, our empirical findings reveal that the two learned style representations provide information at different frequency magnitudes, underscoring the importance of separate style extraction. Our source code is public at: https://github.com/dailenson/SDT. | https://openaccess.thecvf.com/content/CVPR2023/papers/Dai_Disentangling_Writer_and_Character_Styles_for_Handwriting_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Dai_Disentangling_Writer_and_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14736 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Dai_Disentangling_Writer_and_Character_Styles_for_Handwriting_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Dai_Disentangling_Writer_and_Character_Styles_for_Handwriting_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
Nighttime Smartphone Reflective Flare Removal Using Optical Center Symmetry Prior | Yuekun Dai, Yihang Luo, Shangchen Zhou, Chongyi Li, Chen Change Loy | Reflective flare is a phenomenon that occurs when light reflects inside lenses, causing bright spots or a "ghosting effect" in photos, which can impact their quality. Eliminating reflective flare is highly desirable but challenging. Many existing methods rely on manually designed features to detect these bright spots, but they often fail to identify reflective flares created by various types of light and may even mistakenly remove the light sources in scenarios with multiple light sources. To address these challenges, we propose an optical center symmetry prior, which suggests that the reflective flare and light source are always symmetrical around the lens's optical center. This prior helps to locate the reflective flare's proposal region more accurately and can be applied to most smartphone cameras. Building on this prior, we create the first reflective flare removal dataset called BracketFlare, which contains diverse and realistic reflective flare patterns. We use continuous bracketing to capture the reflective flare pattern in the underexposed image and combine it with a normally exposed image to synthesize a pair of flare-corrupted and flare-free images. With the dataset, neural networks can be trained to remove the reflective flares effectively. Extensive experiments demonstrate the effectiveness of our method on both synthetic and real-world datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Dai_Nighttime_Smartphone_Reflective_Flare_Removal_Using_Optical_Center_Symmetry_Prior_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Dai_Nighttime_Smartphone_Reflective_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15046 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Dai_Nighttime_Smartphone_Reflective_Flare_Removal_Using_Optical_Center_Symmetry_Prior_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Dai_Nighttime_Smartphone_Reflective_Flare_Removal_Using_Optical_Center_Symmetry_Prior_CVPR_2023_paper.html | CVPR 2023 | null |
StyleSync: High-Fidelity Generalized and Personalized Lip Sync in Style-Based Generator | Jiazhi Guan, Zhanwang Zhang, Hang Zhou, Tianshu Hu, Kaisiyuan Wang, Dongliang He, Haocheng Feng, Jingtuo Liu, Errui Ding, Ziwei Liu, Jingdong Wang | Despite recent advances in syncing lip movements with any audio waves, current methods still struggle to balance generation quality and the model's generalization ability. Previous studies either require long-term data for training or produce a similar movement pattern on all subjects with low quality. In this paper, we propose StyleSync, an effective framework that enables high-fidelity lip synchronization. We identify that a style-based generator would sufficiently enable such a charming property on both one-shot and few-shot scenarios. Specifically, we design a mask-guided spatial information encoding module that preserves the details of the given face. The mouth shapes are accurately modified by audio through modulated convolutions. Moreover, our design also enables personalized lip-sync by introducing style space and generator refinement on only limited frames. Thus the identity and talking style of a target person could be accurately preserved. Extensive experiments demonstrate the effectiveness of our method in producing high-fidelity results on a variety of scenes. | https://openaccess.thecvf.com/content/CVPR2023/papers/Guan_StyleSync_High-Fidelity_Generalized_and_Personalized_Lip_Sync_in_Style-Based_Generator_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Guan_StyleSync_High-Fidelity_Generalized_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2305.05445 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Guan_StyleSync_High-Fidelity_Generalized_and_Personalized_Lip_Sync_in_Style-Based_Generator_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Guan_StyleSync_High-Fidelity_Generalized_and_Personalized_Lip_Sync_in_Style-Based_Generator_CVPR_2023_paper.html | CVPR 2023 | null |
Balanced Spherical Grid for Egocentric View Synthesis | Changwoon Choi, Sang Min Kim, Young Min Kim | We present EgoNeRF, a practical solution to reconstruct large-scale real-world environments for VR assets. Given a few seconds of casually captured 360 video, EgoNeRF can efficiently build neural radiance fields which enable high-quality rendering from novel viewpoints. Motivated by the recent acceleration of NeRF using feature grids, we adopt spherical coordinate instead of conventional Cartesian coordinate. Cartesian feature grid is inefficient to represent large-scale unbounded scenes because it has a spatially uniform resolution, regardless of distance from viewers. The spherical parameterization better aligns with the rays of egocentric images, and yet enables factorization for performance enhancement. However, the naive spherical grid suffers from irregularities at two poles, and also cannot represent unbounded scenes. To avoid singularities near poles, we combine two balanced grids, which results in a quasi-uniform angular grid. We also partition the radial grid exponentially and place an environment map at infinity to represent unbounded scenes. Furthermore, with our resampling technique for grid-based methods, we can increase the number of valid samples to train NeRF volume. We extensively evaluate our method in our newly introduced synthetic and real-world egocentric 360 video datasets, and it consistently achieves state-of-the-art performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Choi_Balanced_Spherical_Grid_for_Egocentric_View_Synthesis_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Choi_Balanced_Spherical_Grid_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.12408 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Choi_Balanced_Spherical_Grid_for_Egocentric_View_Synthesis_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Choi_Balanced_Spherical_Grid_for_Egocentric_View_Synthesis_CVPR_2023_paper.html | CVPR 2023 | null |
Box-Level Active Detection | Mengyao Lyu, Jundong Zhou, Hui Chen, Yijie Huang, Dongdong Yu, Yaqian Li, Yandong Guo, Yuchen Guo, Liuyu Xiang, Guiguang Ding | Active learning selects informative samples for annotation within budget, which has proven efficient recently on object detection. However, the widely used active detection benchmarks conduct image-level evaluation, which is unrealistic in human workload estimation and biased towards crowded images. Furthermore, existing methods still perform image-level annotation, but equally scoring all targets within the same image incurs waste of budget and redundant labels. Having revealed above problems and limitations, we introduce a box-level active detection framework that controls a box-based budget per cycle, prioritizes informative targets and avoids redundancy for fair comparison and efficient application. Under the proposed box-level setting, we devise a novel pipeline, namely Complementary Pseudo Active Strategy (ComPAS). It exploits both human annotations and the model intelligence in a complementary fashion: an efficient input-end committee queries labels for informative objects only; meantime well-learned targets are identified by the model and compensated with pseudo-labels. ComPAS consistently outperforms 10 competitors under 4 settings in a unified codebase. With supervision from labeled data only, it achieves 100% supervised performance of VOC0712 with merely 19% box annotations. On the COCO dataset, it yields up to 4.3% mAP improvement over the second-best method. ComPAS also supports training with the unlabeled pool, where it surpasses 90% COCO supervised performance with 85% label reduction. Our source code is publicly available at https://github.com/lyumengyao/blad. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lyu_Box-Level_Active_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lyu_Box-Level_Active_Detection_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13089 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lyu_Box-Level_Active_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lyu_Box-Level_Active_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Coreset Sampling From Open-Set for Fine-Grained Self-Supervised Learning | Sungnyun Kim, Sangmin Bae, Se-Young Yun | Deep learning in general domains has constantly been extended to domain-specific tasks requiring the recognition of fine-grained characteristics. However, real-world applications for fine-grained tasks suffer from two challenges: a high reliance on expert knowledge for annotation and necessity of a versatile model for various downstream tasks in a specific domain (e.g., prediction of categories, bounding boxes, or pixel-wise annotations). Fortunately, the recent self-supervised learning (SSL) is a promising approach to pretrain a model without annotations, serving as an effective initialization for any downstream tasks. Since SSL does not rely on the presence of annotation, in general, it utilizes the large-scale unlabeled dataset, referred to as an open-set. In this sense, we introduce a novel Open-Set Self-Supervised Learning problem under the assumption that a large-scale unlabeled open-set is available, as well as the fine-grained target dataset, during a pretraining phase. In our problem setup, it is crucial to consider the distribution mismatch between the open-set and target dataset. Hence, we propose SimCore algorithm to sample a coreset, the subset of an open-set that has a minimum distance to the target dataset in the latent space. We demonstrate that SimCore significantly improves representation learning performance through extensive experimental settings, including eleven fine-grained datasets and seven open-sets in various downstream tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kim_Coreset_Sampling_From_Open-Set_for_Fine-Grained_Self-Supervised_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kim_Coreset_Sampling_From_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11101 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Coreset_Sampling_From_Open-Set_for_Fine-Grained_Self-Supervised_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Coreset_Sampling_From_Open-Set_for_Fine-Grained_Self-Supervised_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion | Davis Rempe, Zhengyi Luo, Xue Bin Peng, Ye Yuan, Kris Kitani, Karsten Kreis, Sanja Fidler, Or Litany | We introduce a method for generating realistic pedestrian trajectories and full-body animations that can be controlled to meet user-defined goals. We draw on recent advances in guided diffusion modeling to achieve test-time controllability of trajectories, which is normally only associated with rule-based systems. Our guided diffusion model allows users to constrain trajectories through target waypoints, speed, and specified social groups while accounting for the surrounding environment context. This trajectory diffusion model is integrated with a novel physics-based humanoid controller to form a closed-loop, full-body pedestrian animation system capable of placing large crowds in a simulated environment with varying terrains. We further propose utilizing the value function learned during RL training of the animation controller to guide diffusion to produce trajectories better suited for particular scenarios such as collision avoidance and traversing uneven terrain. | https://openaccess.thecvf.com/content/CVPR2023/papers/Rempe_Trace_and_Pace_Controllable_Pedestrian_Animation_via_Guided_Trajectory_Diffusion_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Rempe_Trace_and_Pace_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.01893 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Rempe_Trace_and_Pace_Controllable_Pedestrian_Animation_via_Guided_Trajectory_Diffusion_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Rempe_Trace_and_Pace_Controllable_Pedestrian_Animation_via_Guided_Trajectory_Diffusion_CVPR_2023_paper.html | CVPR 2023 | null |
Overlooked Factors in Concept-Based Explanations: Dataset Choice, Concept Learnability, and Human Capability | Vikram V. Ramaswamy, Sunnie S. Y. Kim, Ruth Fong, Olga Russakovsky | Concept-based interpretability methods aim to explain a deep neural network model's components and predictions using a pre-defined set of semantic concepts. These methods evaluate a trained model on a new, "probe" dataset and correlate the model's outputs with concepts labeled in that dataset. Despite their popularity, they suffer from limitations that are not well-understood and articulated in the literature. In this work, we identify and analyze three commonly overlooked factors in concept-based explanations. First, we find that the choice of the probe dataset has a profound impact on the generated explanations. Our analysis reveals that different probe datasets lead to very different explanations, suggesting that the generated explanations are not generalizable outside the probe dataset. Second, we find that concepts in the probe dataset are often harder to learn than the target classes they are used to explain, calling into question the correctness of the explanations. We argue that only easily learnable concepts should be used in concept-based explanations. Finally, while existing methods use hundreds or even thousands of concepts, our human studies reveal a much stricter upper bound of 32 concepts or less, beyond which the explanations are much less practically useful. We discuss the implications of our findings and provide suggestions for future development of concept-based interpretability methods. Code for our analysis and user interface can be found at https://github.com/princetonvisualai/OverlookedFactors. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ramaswamy_Overlooked_Factors_in_Concept-Based_Explanations_Dataset_Choice_Concept_Learnability_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ramaswamy_Overlooked_Factors_in_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2207.09615 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ramaswamy_Overlooked_Factors_in_Concept-Based_Explanations_Dataset_Choice_Concept_Learnability_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ramaswamy_Overlooked_Factors_in_Concept-Based_Explanations_Dataset_Choice_Concept_Learnability_and_CVPR_2023_paper.html | CVPR 2023 | null |
Unsupervised 3D Shape Reconstruction by Part Retrieval and Assembly | Xianghao Xu, Paul Guerrero, Matthew Fisher, Siddhartha Chaudhuri, Daniel Ritchie | Representing a 3D shape with a set of primitives can aid perception of structure, improve robotic object manipulation, and enable editing, stylization, and compression of 3D shapes. Existing methods either use simple parametric primitives or learn a generative shape space of parts. Both have limitations: parametric primitives lead to coarse approximations, while learned parts offer too little control over the decomposition. We instead propose to decompose shapes using a library of 3D parts provided by the user, giving full control over the choice of parts. The library can contain parts with high-quality geometry that are suitable for a given category, resulting in meaningful decom- positions with clean geometry. The type of decomposition can also be controlled through the choice of parts in the library. Our method works via a unsupervised approach that iteratively retrieves parts from the library and refines their placements. We show that this approach gives higher reconstruction accuracy and more desirable decompositions than existing approaches. Additionally, we show how the decom- position can be controlled through the part library by using different part libraries to reconstruct the same shapes. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Unsupervised_3D_Shape_Reconstruction_by_Part_Retrieval_and_Assembly_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_Unsupervised_3D_Shape_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.01999 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Unsupervised_3D_Shape_Reconstruction_by_Part_Retrieval_and_Assembly_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Unsupervised_3D_Shape_Reconstruction_by_Part_Retrieval_and_Assembly_CVPR_2023_paper.html | CVPR 2023 | null |
SeqTrack: Sequence to Sequence Learning for Visual Object Tracking | Xin Chen, Houwen Peng, Dong Wang, Huchuan Lu, Han Hu | In this paper, we present a new sequence-to-sequence learning framework for visual tracking, dubbed SeqTrack. It casts visual tracking as a sequence generation problem, which predicts object bounding boxes in an autoregressive fashion. This is different from prior Siamese trackers and transformer trackers, which rely on designing complicated head networks, such as classification and regression heads. SeqTrack only adopts a simple encoder-decoder transformer architecture. The encoder extracts visual features with a bidirectional transformer, while the decoder generates a sequence of bounding box values autoregressively with a causal transformer. The loss function is a plain cross-entropy. Such a sequence learning paradigm not only simplifies tracking framework, but also achieves competitive performance on benchmarks. For instance, SeqTrack gets 72.5% AUC on LaSOT, establishing a new state-of-the-art performance. Code and models are available at https://github.com/microsoft/VideoX. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_SeqTrack_Sequence_to_Sequence_Learning_for_Visual_Object_Tracking_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2304.14394 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_SeqTrack_Sequence_to_Sequence_Learning_for_Visual_Object_Tracking_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_SeqTrack_Sequence_to_Sequence_Learning_for_Visual_Object_Tracking_CVPR_2023_paper.html | CVPR 2023 | null |
Self-Supervised Non-Uniform Kernel Estimation With Flow-Based Motion Prior for Blind Image Deblurring | Zhenxuan Fang, Fangfang Wu, Weisheng Dong, Xin Li, Jinjian Wu, Guangming Shi | Many deep learning-based solutions to blind image deblurring estimate the blur representation and reconstruct the target image from its blurry observation. However, these methods suffer from severe performance degradation in real-world scenarios because they ignore important prior information about motion blur (e.g., real-world motion blur is diverse and spatially varying). Some methods have attempted to explicitly estimate non-uniform blur kernels by CNNs, but accurate estimation is still challenging due to the lack of ground truth about spatially varying blur kernels in real-world images. To address these issues, we propose to represent the field of motion blur kernels in a latent space by normalizing flows, and design CNNs to predict the latent codes instead of motion kernels. To further improve the accuracy and robustness of non-uniform kernel estimation, we introduce uncertainty learning into the process of estimating latent codes and propose a multi-scale kernel attention module to better integrate image features with estimated kernels. Extensive experimental results, especially on real-world blur datasets, demonstrate that our method achieves state-of-the-art results in terms of both subjective and objective quality as well as excellent generalization performance for non-uniform image deblurring. The code is available at https://see.xidian.edu.cn/faculty/wsdong/Projects/UFPNet.htm. | https://openaccess.thecvf.com/content/CVPR2023/papers/Fang_Self-Supervised_Non-Uniform_Kernel_Estimation_With_Flow-Based_Motion_Prior_for_Blind_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Fang_Self-Supervised_Non-Uniform_Kernel_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fang_Self-Supervised_Non-Uniform_Kernel_Estimation_With_Flow-Based_Motion_Prior_for_Blind_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fang_Self-Supervised_Non-Uniform_Kernel_Estimation_With_Flow-Based_Motion_Prior_for_Blind_CVPR_2023_paper.html | CVPR 2023 | null |
AutoLabel: CLIP-Based Framework for Open-Set Video Domain Adaptation | Giacomo Zara, Subhankar Roy, Paolo Rota, Elisa Ricci | Open-set Unsupervised Video Domain Adaptation (OUVDA) deals with the task of adapting an action recognition model from a labelled source domain to an unlabelled target domain that contains "target-private" categories, which are present in the target but absent in the source. In this work we deviate from the prior work of training a specialized open-set classifier or weighted adversarial learning by proposing to use pre-trained Language and Vision Models (CLIP). The CLIP is well suited for OUVDA due to its rich representation and the zero-shot recognition capabilities. However, rejecting target-private instances with the CLIP's zero-shot protocol requires oracle knowledge about the target-private label names. To circumvent the impossibility of the knowledge of label names, we propose AutoLabel that automatically discovers and generates object-centric compositional candidate target-private class names. Despite its simplicity, we show that CLIP when equipped with AutoLabel can satisfactorily reject the target-private instances, thereby facilitating better alignment between the shared classes of the two domains. The code is available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zara_AutoLabel_CLIP-Based_Framework_for_Open-Set_Video_Domain_Adaptation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zara_AutoLabel_CLIP-Based_Framework_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.01110 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zara_AutoLabel_CLIP-Based_Framework_for_Open-Set_Video_Domain_Adaptation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zara_AutoLabel_CLIP-Based_Framework_for_Open-Set_Video_Domain_Adaptation_CVPR_2023_paper.html | CVPR 2023 | null |
Generative Semantic Segmentation | Jiaqi Chen, Jiachen Lu, Xiatian Zhu, Li Zhang | We present Generative Semantic Segmentation (GSS), a generative learning approach for semantic segmentation. Uniquely, we cast semantic segmentation as an image-conditioned mask generation problem. This is achieved by replacing the conventional per-pixel discriminative learning with a latent prior learning process. Specifically, we model the variational posterior distribution of latent variables given the segmentation mask. To that end, the segmentation mask is expressed with a special type of image (dubbed as maskige). This posterior distribution allows to generate segmentation masks unconditionally. To achieve semantic segmentation on a given image, we further introduce a conditioning network. It is optimized by minimizing the divergence between the posterior distribution of maskige (i.e., segmentation masks) and the latent prior distribution of input training images. Extensive experiments on standard benchmarks show that our GSS can perform competitively to prior art alternatives in the standard semantic segmentation setting, whilst achieving a new state of the art in the more challenging cross-domain setting. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Generative_Semantic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Generative_Semantic_Segmentation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11316 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Generative_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Generative_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Instant-NVR: Instant Neural Volumetric Rendering for Human-Object Interactions From Monocular RGBD Stream | Yuheng Jiang, Kaixin Yao, Zhuo Su, Zhehao Shen, Haimin Luo, Lan Xu | Convenient 4D modeling of human-object interactions is essential for numerous applications. However, monocular tracking and rendering of complex interaction scenarios remain challenging. In this paper, we propose Instant-NVR, a neural approach for instant volumetric human-object tracking and rendering using a single RGBD camera. It bridges traditional non-rigid tracking with recent instant radiance field techniques via a multi-thread tracking-rendering mechanism. In the tracking front-end, we adopt a robust human-object capture scheme to provide sufficient motion priors. We further introduce a separated instant neural representation with a novel hybrid deformation module for the interacting scene. We also provide an on-the-fly reconstruction scheme of the dynamic/static radiance fields via efficient motion-prior searching. Moreover, we introduce an online key frame selection scheme and a rendering-aware refinement strategy to significantly improve the appearance details for online novel-view synthesis. Extensive experiments demonstrate the effectiveness and efficiency of our approach for the instant generation of human-object radiance fields on the fly, notably achieving real-time photo-realistic novel view synthesis under complex human-object interactions. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jiang_Instant-NVR_Instant_Neural_Volumetric_Rendering_for_Human-Object_Interactions_From_Monocular_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_Instant-NVR_Instant_Neural_Volumetric_Rendering_for_Human-Object_Interactions_From_Monocular_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_Instant-NVR_Instant_Neural_Volumetric_Rendering_for_Human-Object_Interactions_From_Monocular_CVPR_2023_paper.html | CVPR 2023 | null |
Aligning Step-by-Step Instructional Diagrams to Video Demonstrations | Jiahao Zhang, Anoop Cherian, Yanbin Liu, Yizhak Ben-Shabat, Cristian Rodriguez, Stephen Gould | Multimodal alignment facilitates the retrieval of instances from one modality when queried using another. In this paper, we consider a novel setting where such an alignment is between (i) instruction steps that are depicted as assembly diagrams (commonly seen in Ikea assembly manuals) and (ii) video segments from in-the-wild videos; these videos comprising an enactment of the assembly actions in the real world. To learn this alignment, we introduce a novel supervised contrastive learning method that learns to align videos with the subtle details in the assembly diagrams, guided by a set of novel losses. To study this problem and demonstrate the effectiveness of our method, we introduce a novel dataset: IAW---for Ikea assembly in the wild---consisting of 183 hours of videos from diverse furniture assembly collections and nearly 8,300 illustrations from their associated instruction manuals and annotated for their ground truth alignments. We define two tasks on this dataset: First, nearest neighbor retrieval between video segments and illustrations, and, second, alignment of instruction steps and the segments for each video. Extensive experiments on IAW demonstrate superior performances of our approach against alternatives. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Aligning_Step-by-Step_Instructional_Diagrams_to_Video_Demonstrations_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Aligning_Step-by-Step_Instructional_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13800 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Aligning_Step-by-Step_Instructional_Diagrams_to_Video_Demonstrations_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Aligning_Step-by-Step_Instructional_Diagrams_to_Video_Demonstrations_CVPR_2023_paper.html | CVPR 2023 | null |
Collecting Cross-Modal Presence-Absence Evidence for Weakly-Supervised Audio-Visual Event Perception | Junyu Gao, Mengyuan Chen, Changsheng Xu | With only video-level event labels, this paper targets at the task of weakly-supervised audio-visual event perception (WS-AVEP), which aims to temporally localize and categorize events belonging to each modality. Despite the recent progress, most existing approaches either ignore the unsynchronized property of audio-visual tracks or discount the complementary modality for explicit enhancement. We argue that, for an event residing in one modality, the modality itself should provide ample presence evidence of this event, while the other complementary modality is encouraged to afford the absence evidence as a reference signal. To this end, we propose to collect Cross-Modal Presence-Absence Evidence (CMPAE) in a unified framework. Specifically, by leveraging uni-modal and cross-modal representations, a presence-absence evidence collector (PAEC) is designed under Subjective Logic theory. To learn the evidence in a reliable range, we propose a joint-modal mutual learning (JML) process, which calibrates the evidence of diverse audible, visible, and audi-visible events adaptively and dynamically. Extensive experiments show that our method surpasses state-of-the-arts (e.g., absolute gains of 3.6% and 6.1% in terms of event-level visual and audio metrics). Code is available in github.com/MengyuanChen21/CVPR2023-CMPAE. | https://openaccess.thecvf.com/content/CVPR2023/papers/Gao_Collecting_Cross-Modal_Presence-Absence_Evidence_for_Weakly-Supervised_Audio-Visual_Event_Perception_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_Collecting_Cross-Modal_Presence-Absence_Evidence_for_Weakly-Supervised_Audio-Visual_Event_Perception_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_Collecting_Cross-Modal_Presence-Absence_Evidence_for_Weakly-Supervised_Audio-Visual_Event_Perception_CVPR_2023_paper.html | CVPR 2023 | null |
High-Fidelity and Freely Controllable Talking Head Video Generation | Yue Gao, Yuan Zhou, Jinglu Wang, Xiao Li, Xiang Ming, Yan Lu | Talking head generation is to generate video based on a given source identity and target motion. However, current methods face several challenges that limit the quality and controllability of the generated videos. First, the generated face often has unexpected deformation and severe distortions. Second, the driving image does not explicitly disentangle movement-relevant information, such as poses and expressions, which restricts the manipulation of different attributes during generation. Third, the generated videos tend to have flickering artifacts due to the inconsistency of the extracted landmarks between adjacent frames. In this paper, we propose a novel model that produces high-fidelity talking head videos with free control over head pose and expression. Our method leverages both self-supervised learned landmarks and 3D face model-based landmarks to model the motion. We also introduce a novel motion-aware multi-scale feature alignment module to effectively transfer the motion without face distortion. Furthermore, we enhance the smoothness of the synthesized talking head videos with a feature context adaptation and propagation module. We evaluate our model on challenging datasets and demonstrate its state-of-the-art performance. More information is available at https://yuegao.me/PECHead. | https://openaccess.thecvf.com/content/CVPR2023/papers/Gao_High-Fidelity_and_Freely_Controllable_Talking_Head_Video_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gao_High-Fidelity_and_Freely_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.10168 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_High-Fidelity_and_Freely_Controllable_Talking_Head_Video_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_High-Fidelity_and_Freely_Controllable_Talking_Head_Video_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
Q-DETR: An Efficient Low-Bit Quantized Detection Transformer | Sheng Xu, Yanjing Li, Mingbao Lin, Peng Gao, Guodong Guo, Jinhu Lü, Baochang Zhang | The recent detection transformer (DETR) has advanced object detection, but its application on resource-constrained devices requires massive computation and memory resources. Quantization stands out as a solution by representing the network in low-bit parameters and operations. However, there is a significant performance drop when performing low-bit quantized DETR (Q-DETR) with existing quantization methods. We find that the bottlenecks of Q-DETR come from the query information distortion through our empirical analyses. This paper addresses this problem based on a distribution rectification distillation (DRD). We formulate our DRD as a bi-level optimization problem, which can be derived by generalizing the information bottleneck (IB) principle to the learning of Q-DETR. At the inner level, we conduct a distribution alignment for the queries to maximize the self-information entropy. At the upper level, we introduce a new foreground-aware query matching scheme to effectively transfer the teacher information to distillation-desired features to minimize the conditional information entropy. Extensive experimental results show that our method performs much better than prior arts. For example, the 4-bit Q-DETR can theoretically accelerate DETR with ResNet-50 backbone by 6.6x and achieve 39.4% AP, with only 2.6% performance gaps than its real-valued counterpart on the COCO dataset. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Q-DETR_An_Efficient_Low-Bit_Quantized_Detection_Transformer_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Q-DETR_An_Efficient_Low-Bit_Quantized_Detection_Transformer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Q-DETR_An_Efficient_Low-Bit_Quantized_Detection_Transformer_CVPR_2023_paper.html | CVPR 2023 | null |
DINER: Depth-Aware Image-Based NEural Radiance Fields | Malte Prinzler, Otmar Hilliges, Justus Thies | We present Depth-aware Image-based NEural Radiance fields (DINER). Given a sparse set of RGB input views, we predict depth and feature maps to guide the reconstruction of a volumetric scene representation that allows us to render 3D objects under novel views. Specifically, we propose novel techniques to incorporate depth information into feature fusion and efficient scene sampling. In comparison to the previous state of the art, DINER achieves higher synthesis quality and can process input views with greater disparity. This allows us to capture scenes more completely without changing capturing hardware requirements and ultimately enables larger viewpoint changes during novel view synthesis. We evaluate our method by synthesizing novel views, both for human heads and for general objects, and observe significantly improved qualitative results and increased perceptual metrics compared to the previous state of the art. | https://openaccess.thecvf.com/content/CVPR2023/papers/Prinzler_DINER_Depth-Aware_Image-Based_NEural_Radiance_Fields_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Prinzler_DINER_Depth-Aware_Image-Based_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.16630 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Prinzler_DINER_Depth-Aware_Image-Based_NEural_Radiance_Fields_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Prinzler_DINER_Depth-Aware_Image-Based_NEural_Radiance_Fields_CVPR_2023_paper.html | CVPR 2023 | null |
Burstormer: Burst Image Restoration and Enhancement Transformer | Akshay Dudhane, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan, Ming-Hsuan Yang | On a shutter press, modern handheld cameras capture multiple images in rapid succession and merge them to generate a single image. However, individual frames in a burst are misaligned due to inevitable motions and contain multiple degradations. The challenge is to properly align the successive image shots and merge their complimentary information to achieve high-quality outputs. Towards this direction, we propose Burstormer: a novel transformer-based architecture for burst image restoration and enhancement. In comparison to existing works, our approach exploits multi-scale local and non-local features to achieve improved alignment and feature fusion. Our key idea is to enable inter-frame communication in the burst neighborhoods for information aggregation and progressive fusion while modeling the burst-wide context. However, the input burst frames need to be properly aligned before fusing their information. Therefore, we propose an enhanced deformable alignment module for aligning burst features with regards to the reference frame. Unlike existing methods, the proposed alignment module not only aligns burst features but also exchanges feature information and maintains focused communication with the reference frame through the proposed reference-based feature enrichment mechanism, which facilitates handling complex motions. After multi-level alignment and enrichment, we re-emphasize on inter-frame communication within burst using a cyclic burst sampling module. Finally, the inter-frame information is aggregated using the proposed burst feature fusion module followed by progressive upsampling. Our Burstormer outperforms state-of-the-art methods on burst super-resolution, burst denoising and burst low-light enhancement. Our codes and pre-trained models are available at https://github.com/akshaydudhane16/Burstormer. | https://openaccess.thecvf.com/content/CVPR2023/papers/Dudhane_Burstormer_Burst_Image_Restoration_and_Enhancement_Transformer_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Dudhane_Burstormer_Burst_Image_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.01194 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Dudhane_Burstormer_Burst_Image_Restoration_and_Enhancement_Transformer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Dudhane_Burstormer_Burst_Image_Restoration_and_Enhancement_Transformer_CVPR_2023_paper.html | CVPR 2023 | null |
Progressive Transformation Learning for Leveraging Virtual Images in Training | Yi-Ting Shen, Hyungtae Lee, Heesung Kwon, Shuvra S. Bhattacharyya | To effectively interrogate UAV-based images for detecting objects of interest, such as humans, it is essential to acquire large-scale UAV-based datasets that include human instances with various poses captured from widely varying viewing angles. As a viable alternative to laborious and costly data curation, we introduce Progressive Transformation Learning (PTL), which gradually augments a training dataset by adding transformed virtual images with enhanced realism. Generally, a virtual2real transformation generator in the conditional GAN framework suffers from quality degradation when a large domain gap exists between real and virtual images. To deal with the domain gap, PTL takes a novel approach that progressively iterates the following three steps: 1) select a subset from a pool of virtual images according to the domain gap, 2) transform the selected virtual images to enhance realism, and 3) add the transformed virtual images to the training set while removing them from the pool. In PTL, accurately quantifying the domain gap is critical. To do that, we theoretically demonstrate that the feature representation space of a given object detector can be modeled as a multivariate Gaussian distribution from which the Mahalanobis distance between a virtual object and the Gaussian distribution of each object category in the representation space can be readily computed. Experiments show that PTL results in a substantial performance increase over the baseline, especially in the small data and the cross-domain regime. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shen_Progressive_Transformation_Learning_for_Leveraging_Virtual_Images_in_Training_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shen_Progressive_Transformation_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.01778 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shen_Progressive_Transformation_Learning_for_Leveraging_Virtual_Images_in_Training_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shen_Progressive_Transformation_Learning_for_Leveraging_Virtual_Images_in_Training_CVPR_2023_paper.html | CVPR 2023 | null |
Co-Speech Gesture Synthesis by Reinforcement Learning With Contrastive Pre-Trained Rewards | Mingyang Sun, Mengchen Zhao, Yaqing Hou, Minglei Li, Huang Xu, Songcen Xu, Jianye Hao | There is a growing demand of automatically synthesizing co-speech gestures for virtual characters. However, it remains a challenge due to the complex relationship between input speeches and target gestures. Most existing works focus on predicting the next gesture that fits the data best, however, such methods are myopic and lack the ability to plan for future gestures. In this paper, we propose a novel reinforcement learning (RL) framework called RACER to generate sequences of gestures that maximize the overall satisfactory. RACER employs a vector quantized variational autoencoder to learn compact representations of gestures and a GPT-based policy architecture to generate coherent sequence of gestures autoregressively. In particular, we propose a contrastive pre-training approach to calculate the rewards, which integrates contextual information into action evaluation and successfully captures the complex relationships between multi-modal speech-gesture data. Experimental results show that our method significantly outperforms existing baselines in terms of both objective metrics and subjective human judgements. Demos can be found at https://github.com/RLracer/RACER.git. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sun_Co-Speech_Gesture_Synthesis_by_Reinforcement_Learning_With_Contrastive_Pre-Trained_Rewards_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Co-Speech_Gesture_Synthesis_by_Reinforcement_Learning_With_Contrastive_Pre-Trained_Rewards_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Co-Speech_Gesture_Synthesis_by_Reinforcement_Learning_With_Contrastive_Pre-Trained_Rewards_CVPR_2023_paper.html | CVPR 2023 | null |
Reconstructing Signing Avatars From Video Using Linguistic Priors | Maria-Paola Forte, Peter Kulits, Chun-Hao P. Huang, Vasileios Choutas, Dimitrios Tzionas, Katherine J. Kuchenbecker, Michael J. Black | Sign language (SL) is the primary method of communication for the 70 million Deaf people around the world. Video dictionaries of isolated signs are a core SL learning tool. Replacing these with 3D avatars can aid learning and enable AR/VR applications, improving access to technology and online media. However, little work has attempted to estimate expressive 3D avatars from SL video; occlusion, noise, and motion blur make this task difficult. We address this by introducing novel linguistic priors that are universally applicable to SL and provide constraints on 3D hand pose that help resolve ambiguities within isolated signs. Our method, SGNify, captures fine-grained hand pose, facial expression, and body movement fully automatically from in-the-wild monocular SL videos. We evaluate SGNify quantitatively by using a commercial motion-capture system to compute 3D avatars synchronized with monocular video. SGNify outperforms state-of-the-art 3D body-pose- and shape-estimation methods on SL videos. A perceptual study shows that SGNify's 3D reconstructions are significantly more comprehensible and natural than those of previous methods and are on par with the source videos. Code and data are available at sgnify.is.tue.mpg.de. | https://openaccess.thecvf.com/content/CVPR2023/papers/Forte_Reconstructing_Signing_Avatars_From_Video_Using_Linguistic_Priors_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Forte_Reconstructing_Signing_Avatars_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.10482 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Forte_Reconstructing_Signing_Avatars_From_Video_Using_Linguistic_Priors_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Forte_Reconstructing_Signing_Avatars_From_Video_Using_Linguistic_Priors_CVPR_2023_paper.html | CVPR 2023 | null |
DeepMapping2: Self-Supervised Large-Scale LiDAR Map Optimization | Chao Chen, Xinhao Liu, Yiming Li, Li Ding, Chen Feng | LiDAR mapping is important yet challenging in self-driving and mobile robotics. To tackle such a global point cloud registration problem, DeepMapping converts the complex map estimation into a self-supervised training of simple deep networks. Despite its broad convergence range on small datasets, DeepMapping still cannot produce satisfactory results on large-scale datasets with thousands of frames. This is due to the lack of loop closures and exact cross-frame point correspondences, and the slow convergence of its global localization network. We propose DeepMapping2 by adding two novel techniques to address these issues: (1) organization of training batch based on map topology from loop closing, and (2) self-supervised local-to-global point consistency loss leveraging pairwise registration. Our experiments and ablation studies on public datasets such as KITTI, NCLT, and Nebula, demonstrate the effectiveness of our method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_DeepMapping2_Self-Supervised_Large-Scale_LiDAR_Map_Optimization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_DeepMapping2_Self-Supervised_Large-Scale_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.06331 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_DeepMapping2_Self-Supervised_Large-Scale_LiDAR_Map_Optimization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_DeepMapping2_Self-Supervised_Large-Scale_LiDAR_Map_Optimization_CVPR_2023_paper.html | CVPR 2023 | null |
SDC-UDA: Volumetric Unsupervised Domain Adaptation Framework for Slice-Direction Continuous Cross-Modality Medical Image Segmentation | Hyungseob Shin, Hyeongyu Kim, Sewon Kim, Yohan Jun, Taejoon Eo, Dosik Hwang | Recent advances in deep learning-based medical image segmentation studies achieve nearly human-level performance in fully supervised manner. However, acquiring pixel-level expert annotations is extremely expensive and laborious in medical imaging fields. Unsupervised domain adaptation (UDA) can alleviate this problem, which makes it possible to use annotated data in one imaging modality to train a network that can successfully perform segmentation on target imaging modality with no labels. In this work, we propose SDC-UDA, a simple yet effective volumetric UDA framework for Slice-Direction Continuous cross-modality medical image segmentation which combines intra- and inter-slice self-attentive image translation, uncertainty-constrained pseudo-label refinement, and volumetric self-training. Our method is distinguished from previous methods on UDA for medical image segmentation in that it can obtain continuous segmentation in the slice direction, thereby ensuring higher accuracy and potential in clinical practice. We validate SDC-UDA with multiple publicly available cross-modality medical image segmentation datasets and achieve state-of-the-art segmentation performance, not to mention the superior slice-direction continuity of prediction compared to previous studies. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shin_SDC-UDA_Volumetric_Unsupervised_Domain_Adaptation_Framework_for_Slice-Direction_Continuous_Cross-Modality_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shin_SDC-UDA_Volumetric_Unsupervised_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shin_SDC-UDA_Volumetric_Unsupervised_Domain_Adaptation_Framework_for_Slice-Direction_Continuous_Cross-Modality_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shin_SDC-UDA_Volumetric_Unsupervised_Domain_Adaptation_Framework_for_Slice-Direction_Continuous_Cross-Modality_CVPR_2023_paper.html | CVPR 2023 | null |
DoNet: Deep De-Overlapping Network for Cytology Instance Segmentation | Hao Jiang, Rushan Zhang, Yanning Zhou, Yumeng Wang, Hao Chen | Cell instance segmentation in cytology images has significant importance for biology analysis and cancer screening, while remains challenging due to 1) the extensive overlapping translucent cell clusters that cause the ambiguous boundaries, and 2) the confusion of mimics and debris as nuclei. In this work, we proposed a De-overlapping Network (DoNet) in a decompose-and-recombined strategy. A Dual-path Region Segmentation Module (DRM) explicitly decomposes the cell clusters into intersection and complement regions, followed by a Semantic Consistency-guided Recombination Module (CRM) for integration. To further introduce the containment relationship of the nucleus in the cytoplasm, we design a Mask-guided Region Proposal Strategy (MRP) that integrates the cell attention maps for inner-cell instance prediction. We validate the proposed approach on ISBI2014 and CPS datasets. Experiments show that our proposed DoNet significantly outperforms other state-of-the-art (SOTA) cell instance segmentation methods. The code is available at https://github.com/DeepDoNet/DoNet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jiang_DoNet_Deep_De-Overlapping_Network_for_Cytology_Instance_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jiang_DoNet_Deep_De-Overlapping_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14373 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_DoNet_Deep_De-Overlapping_Network_for_Cytology_Instance_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_DoNet_Deep_De-Overlapping_Network_for_Cytology_Instance_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
AVFace: Towards Detailed Audio-Visual 4D Face Reconstruction | Aggelina Chatziagapi, Dimitris Samaras | In this work, we present a multimodal solution to the problem of 4D face reconstruction from monocular videos. 3D face reconstruction from 2D images is an under-constrained problem due to the ambiguity of depth. State-of-the-art methods try to solve this problem by leveraging visual information from a single image or video, whereas 3D mesh animation approaches rely more on audio. However, in most cases (e.g. AR/VR applications), videos include both visual and speech information. We propose AVFace that incorporates both modalities and accurately reconstructs the 4D facial and lip motion of any speaker, without requiring any 3D ground truth for training. A coarse stage estimates the per-frame parameters of a 3D morphable model, followed by a lip refinement, and then a fine stage recovers facial geometric details. Due to the temporal audio and video information captured by transformer-based modules, our method is robust in cases when either modality is insufficient (e.g. face occlusions). Extensive qualitative and quantitative evaluation demonstrates the superiority of our method over the current state-of-the-art. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chatziagapi_AVFace_Towards_Detailed_Audio-Visual_4D_Face_Reconstruction_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2304.13115 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chatziagapi_AVFace_Towards_Detailed_Audio-Visual_4D_Face_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chatziagapi_AVFace_Towards_Detailed_Audio-Visual_4D_Face_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
Divide and Conquer: Answering Questions With Object Factorization and Compositional Reasoning | Shi Chen, Qi Zhao | Humans have the innate capability to answer diverse questions, which is rooted in the natural ability to correlate different concepts based on their semantic relationships and decompose difficult problems into sub-tasks. On the contrary, existing visual reasoning methods assume training samples that capture every possible object and reasoning problem, and rely on black-boxed models that commonly exploit statistical priors. They have yet to develop the capability to address novel objects or spurious biases in real-world scenarios, and also fall short of interpreting the rationales behind their decisions. Inspired by humans' reasoning of the visual world, we tackle the aforementioned challenges from a compositional perspective, and propose an integral framework consisting of a principled object factorization method and a novel neural module network. Our factorization method decomposes objects based on their key characteristics, and automatically derives prototypes that represent a wide range of objects. With these prototypes encoding important semantics, the proposed network then correlates objects by measuring their similarity on a common semantic space and makes decisions with a compositional reasoning process. It is capable of answering questions with diverse objects regardless of their availability during training, and overcoming the issues of biased question-answer distributions. In addition to the enhanced generalizability, our framework also provides an interpretable interface for understanding the decision-making process of models. Our code is available at https://github.com/szzexpoi/POEM. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Divide_and_Conquer_Answering_Questions_With_Object_Factorization_and_Compositional_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Divide_and_Conquer_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.10482 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Divide_and_Conquer_Answering_Questions_With_Object_Factorization_and_Compositional_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Divide_and_Conquer_Answering_Questions_With_Object_Factorization_and_Compositional_CVPR_2023_paper.html | CVPR 2023 | null |
Instant Domain Augmentation for LiDAR Semantic Segmentation | Kwonyoung Ryu, Soonmin Hwang, Jaesik Park | Despite the increasing popularity of LiDAR sensors, perception algorithms using 3D LiDAR data struggle with the 'sensor-bias problem'. Specifically, the performance of perception algorithms significantly drops when an unseen specification of LiDAR sensor is applied at test time due to the domain discrepancy. This paper presents a fast and flexible LiDAR augmentation method for the semantic segmentation task, called 'LiDomAug'. It aggregates raw LiDAR scans and creates a LiDAR scan of any configurations with the consideration of dynamic distortion and occlusion, resulting in instant domain augmentation. Our on-demand augmentation module runs at 330 FPS, so it can be seamlessly integrated into the data loader in the learning framework. In our experiments, learning-based approaches aided with the proposed LiDomAug are less affected by the sensor-bias issue and achieve new state-of-the-art domain adaptation performances on SemanticKITTI and nuScenes dataset without the use of the target domain data. We also present a sensor-agnostic model that faithfully works on the various LiDAR configurations. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ryu_Instant_Domain_Augmentation_for_LiDAR_Semantic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ryu_Instant_Domain_Augmentation_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.14378 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ryu_Instant_Domain_Augmentation_for_LiDAR_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ryu_Instant_Domain_Augmentation_for_LiDAR_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
A Characteristic Function-Based Method for Bottom-Up Human Pose Estimation | Haoxuan Qu, Yujun Cai, Lin Geng Foo, Ajay Kumar, Jun Liu | Most recent methods formulate the task of human pose estimation as a heatmap estimation problem, and use the overall L2 loss computed from the entire heatmap to optimize the heatmap prediction. In this paper, we show that in bottom-up human pose estimation where each heatmap often contains multiple body joints, using the overall L2 loss to optimize the heatmap prediction may not be the optimal choice. This is because, minimizing the overall L2 loss cannot always lead the model to locate all the body joints across different sub-regions of the heatmap more accurately. To cope with this problem, from a novel perspective, we propose a new bottom-up human pose estimation method that optimizes the heatmap prediction via minimizing the distance between two characteristic functions respectively constructed from the predicted heatmap and the groundtruth heatmap. Our analysis presented in this paper indicates that the distance between these two characteristic functions is essentially the upper bound of the L2 losses w.r.t. sub-regions of the predicted heatmap. Therefore, via minimizing the distance between the two characteristic functions, we can optimize the model to provide a more accurate localization result for the body joints in different sub-regions of the predicted heatmap. We show the effectiveness of our proposed method through extensive experiments on the COCO dataset and the CrowdPose dataset. | https://openaccess.thecvf.com/content/CVPR2023/papers/Qu_A_Characteristic_Function-Based_Method_for_Bottom-Up_Human_Pose_Estimation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Qu_A_Characteristic_Function-Based_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Qu_A_Characteristic_Function-Based_Method_for_Bottom-Up_Human_Pose_Estimation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Qu_A_Characteristic_Function-Based_Method_for_Bottom-Up_Human_Pose_Estimation_CVPR_2023_paper.html | CVPR 2023 | null |
SceneTrilogy: On Human Scene-Sketch and Its Complementarity With Photo and Text | Pinaki Nath Chowdhury, Ayan Kumar Bhunia, Aneeshan Sain, Subhadeep Koley, Tao Xiang, Yi-Zhe Song | In this paper, we extend scene understanding to include that of human sketch. The result is a complete trilogy of scene representation from three diverse and complementary modalities -- sketch, photo, and text. Instead of learning a rigid three-way embedding and be done with it, we focus on learning a flexible joint embedding that fully supports the "optionality" that this complementarity brings. Our embedding supports optionality on two axis: (i) optionality across modalities -- use any combination of modalities as query for downstream tasks like retrieval, (ii) optionality across tasks -- simultaneously utilising the embedding for either discriminative (e.g., retrieval) or generative tasks (e.g., captioning). This provides flexibility to end-users by exploiting the best of each modality, therefore serving the very purpose behind our proposal of a trilogy at the first place. First, a combination of information-bottleneck and conditional invertible neural networks disentangle the modality-specific component from modality-agnostic in sketch, photo, and text. Second, the modality-agnostic instances from sketch, photo, and text are synergised using a modified cross-attention. Once learned, we show our embedding can accommodate a multi-facet of scene-related tasks, including those enabled for the first time by the inclusion of sketch, all without any task-specific modifications. Project Page: http://www.pinakinathc.me/scenetrilogy | https://openaccess.thecvf.com/content/CVPR2023/papers/Chowdhury_SceneTrilogy_On_Human_Scene-Sketch_and_Its_Complementarity_With_Photo_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chowdhury_SceneTrilogy_On_Human_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2204.11964 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chowdhury_SceneTrilogy_On_Human_Scene-Sketch_and_Its_Complementarity_With_Photo_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chowdhury_SceneTrilogy_On_Human_Scene-Sketch_and_Its_Complementarity_With_Photo_and_CVPR_2023_paper.html | CVPR 2023 | null |
ERM-KTP: Knowledge-Level Machine Unlearning via Knowledge Transfer | Shen Lin, Xiaoyu Zhang, Chenyang Chen, Xiaofeng Chen, Willy Susilo | Machine unlearning can fortify the privacy and security of machine learning applications. Unfortunately, the exact unlearning approaches are inefficient, and the approximate unlearning approaches are unsuitable for complicated CNNs. Moreover, the approximate approaches have serious security flaws because even unlearning completely different data points can produce the same contribution estimation as unlearning the target data points. To address the above problems, we try to define machine unlearning from the knowledge perspective, and we propose a knowledge-level machine unlearning method, namely ERM-KTP. Specifically, we propose an entanglement-reduced mask (ERM) structure to reduce the knowledge entanglement among classes during the training phase. When receiving the unlearning requests, we transfer the knowledge of the non-target data points from the original model to the unlearned model and meanwhile prohibit the knowledge of the target data points via our proposed knowledge transfer and prohibition (KTP) method. Finally, we will get the unlearned model as the result and delete the original model to accomplish the unlearning process. Especially, our proposed ERM-KTP is an interpretable unlearning method because the ERM structure and the crafted masks in KTP can explicitly explain the operation and the effect of unlearning data points. Extensive experiments demonstrate the effectiveness, efficiency, high fidelity, and scalability of the ERM-KTP unlearning method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lin_ERM-KTP_Knowledge-Level_Machine_Unlearning_via_Knowledge_Transfer_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_ERM-KTP_Knowledge-Level_Machine_Unlearning_via_Knowledge_Transfer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_ERM-KTP_Knowledge-Level_Machine_Unlearning_via_Knowledge_Transfer_CVPR_2023_paper.html | CVPR 2023 | null |
RefSR-NeRF: Towards High Fidelity and Super Resolution View Synthesis | Xudong Huang, Wei Li, Jie Hu, Hanting Chen, Yunhe Wang | We present Reference-guided Super-Resolution Neural Radiance Field (RefSR-NeRF) that extends NeRF to super resolution and photorealistic novel view synthesis. Despite NeRF's extraordinary success in the neural rendering field, it suffers from blur in high resolution rendering because its inherent multilayer perceptron struggles to learn high frequency details and incurs a computational explosion as resolution increases. Therefore, we propose RefSR-NeRF, an end-to-end framework that first learns a low resolution NeRF representation, and then reconstructs the high frequency details with the help of a high resolution reference image. We observe that simply introducing the pre-trained models from the literature tends to produce unsatisfied artifacts due to the divergence in the degradation model. To this end, we design a novel lightweight RefSR model to learn the inverse degradation process from NeRF renderings to target HR ones. Extensive experiments on multiple benchmarks demonstrate that our method exhibits an impressive trade-off among rendering quality, speed, and memory usage, outperforming or on par with NeRF and its variants while being 52x speedup with minor extra memory usage. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_RefSR-NeRF_Towards_High_Fidelity_and_Super_Resolution_View_Synthesis_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_RefSR-NeRF_Towards_High_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_RefSR-NeRF_Towards_High_Fidelity_and_Super_Resolution_View_Synthesis_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_RefSR-NeRF_Towards_High_Fidelity_and_Super_Resolution_View_Synthesis_CVPR_2023_paper.html | CVPR 2023 | null |
DATE: Domain Adaptive Product Seeker for E-Commerce | Haoyuan Li, Hao Jiang, Tao Jin, Mengyan Li, Yan Chen, Zhijie Lin, Yang Zhao, Zhou Zhao | Product Retrieval (PR) and Grounding (PG), aiming to seek image and object-level products respectively according to a textual query, have attracted great interest recently for better shopping experience. Owing to the lack of relevant datasets, we collect two large-scale benchmark datasets from Taobao Mall and Live domains with about 474k and 101k image-query pairs for PR, and manually annotate the object bounding boxes in each image for PG. As annotating boxes is expensive and time-consuming, we attempt to transfer knowledge from annotated domain to unannotated for PG to achieve un-supervised Domain Adaptation (PG-DA). We propose a Domain Adaptive producT sEeker (DATE) framework, regarding PR and PG as Product Seeking problem at different levels, to assist the query date the product. Concretely, we first design a semantics-aggregated feature extractor for each modality to obtain concentrated and comprehensive features for following efficient retrieval and fine-grained grounding tasks. Then, we present two cooperative seekers to simultaneously search the image for PR and localize the product for PG. Besides, we devise a domain aligner for PG-DA to alleviate uni-modal marginal and multi-modal conditional distribution shift between source and target domains, and design a pseudo box generator to dynamically select reliable instances and generate bounding boxes for further knowledge transfer. Extensive experiments show that our DATE achieves satisfactory performance in fully-supervised PR, PG and un-supervised PG-DA. Our desensitized datasets will be publicly available here https://github.com/Taobao-live/Product-Seeking. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_DATE_Domain_Adaptive_Product_Seeker_for_E-Commerce_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_DATE_Domain_Adaptive_Product_Seeker_for_E-Commerce_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_DATE_Domain_Adaptive_Product_Seeker_for_E-Commerce_CVPR_2023_paper.html | CVPR 2023 | null |
Polarimetric iToF: Measuring High-Fidelity Depth Through Scattering Media | Daniel S. Jeon, Andréas Meuleman, Seung-Hwan Baek, Min H. Kim | Indirect time-of-flight (iToF) imaging allows us to capture dense depth information at a low cost. However, iToF imaging often suffers from multipath interference (MPI) artifacts in the presence of scattering media, resulting in severe depth-accuracy degradation. For instance, iToF cameras cannot measure depth accurately through fog because ToF active illumination scatters back to the sensor before reaching the farther target surface. In this work, we propose a polarimetric iToF imaging method that can capture depth information robustly through scattering media. Our observations on the principle of indirect ToF imaging and polarization of light allow us to formulate a novel computational model of scattering-aware polarimetric phase measurements that enables us to correct MPI errors. We first devise a scattering-aware polarimetric iToF model that can estimate the phase of unpolarized backscattered light. We then combine the optical filtering of polarization and our computational modeling of unpolarized backscattered light via scattering analysis of phase and amplitude. This allows us to tackle the MPI problem by estimating the scattering energy through the participating media. We validate our method on an experimental setup using a customized off-the-shelf iToF camera. Our method outperforms baseline methods by a significant margin by means of our scattering model and polarimetric phase measurements. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jeon_Polarimetric_iToF_Measuring_High-Fidelity_Depth_Through_Scattering_Media_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jeon_Polarimetric_iToF_Measuring_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jeon_Polarimetric_iToF_Measuring_High-Fidelity_Depth_Through_Scattering_Media_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jeon_Polarimetric_iToF_Measuring_High-Fidelity_Depth_Through_Scattering_Media_CVPR_2023_paper.html | CVPR 2023 | null |
Jedi: Entropy-Based Localization and Removal of Adversarial Patches | Bilel Tarchoun, Anouar Ben Khalifa, Mohamed Ali Mahjoub, Nael Abu-Ghazaleh, Ihsen Alouani | Real-world adversarial physical patches were recently shown to be successful in compromising state-of-the-art models in a variety of computer vision applications. The most promising defenses that are based on either input gradient or features analyses have been shown to be compromised by recent GAN-based adaptive attacks that generate realistic/naturalistic patches. In this paper, we propose Jedi, a new defense against adversarial patches that is resilient to realistic patch attacks, and also improves detection and recovery compared to the state of the art. Jedi leverages two new ideas: (1) it improves the identification of potential patch regions using entropy analysis: we show that the entropy of adversarial patches is high, even in naturalistic patches; and (2) it improves the localization of adversarial patches, using an autoencoder that is able to complete patch regions and filter out normal regions with high entropy that are not part of a patch. Jedi achieves high precision adversarial patch localization, which we show is critical to successfully repair the images. Since Jedi relies on an input entropy analysis, it is model-agnostic, and can be applied on pre-trained off-the-shelf models without changes to the training or inference of the protected models. Jedi detects on average 90% of adversarial patches across different benchmarks and recovers up to 94% of successful patch attacks (Compared to 75% and 65% for LGS and Jujutsu, respectively). Jedi is also able to continue detection even in the presence of adaptive realistic patches that are able to fool other defenses. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tarchoun_Jedi_Entropy-Based_Localization_and_Removal_of_Adversarial_Patches_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tarchoun_Jedi_Entropy-Based_Localization_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2304.10029 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tarchoun_Jedi_Entropy-Based_Localization_and_Removal_of_Adversarial_Patches_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tarchoun_Jedi_Entropy-Based_Localization_and_Removal_of_Adversarial_Patches_CVPR_2023_paper.html | CVPR 2023 | null |
Localized Semantic Feature Mixers for Efficient Pedestrian Detection in Autonomous Driving | Abdul Hannan Khan, Mohammed Shariq Nawaz, Andreas Dengel | Autonomous driving systems rely heavily on the underlying perception module which needs to be both performant and efficient to allow precise decisions in real-time. Avoiding collisions with pedestrians is of topmost priority in any autonomous driving system. Therefore, pedestrian detection is one of the core parts of such systems' perception modules. Current state-of-the-art pedestrian detectors have two major issues. Firstly, they have long inference times which affect the efficiency of the whole perception module, and secondly, their performance in the case of small and heavily occluded pedestrians is poor. We propose Localized Semantic Feature Mixers (LSFM), a novel, anchor-free pedestrian detection architecture. It uses our novel Super Pixel Pyramid Pooling module instead of the, computationally costly, Feature Pyramid Networks for feature encoding. Moreover, our MLPMixer-based Dense Focal Detection Network is used as a light detection head, reducing computational effort and inference time compared to existing approaches. To boost the performance of the proposed architecture, we adapt and use mixup augmentation which improves the performance, especially in small and heavily occluded cases. We benchmark LSFM against the state-of-the-art on well-established traffic scene pedestrian datasets. The proposed LSFM achieves state-of-the-art performance in Caltech, City Persons, Euro City Persons, and TJU-Traffic-Pedestrian datasets while reducing the inference time on average by 55%. Further, LSFM beats the human baseline for the first time in the history of pedestrian detection. Finally, we conducted a cross-dataset evaluation which proved that our proposed LSFM generalizes well to unseen data. | https://openaccess.thecvf.com/content/CVPR2023/papers/Khan_Localized_Semantic_Feature_Mixers_for_Efficient_Pedestrian_Detection_in_Autonomous_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Khan_Localized_Semantic_Feature_Mixers_for_Efficient_Pedestrian_Detection_in_Autonomous_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Khan_Localized_Semantic_Feature_Mixers_for_Efficient_Pedestrian_Detection_in_Autonomous_CVPR_2023_paper.html | CVPR 2023 | null |
Self-Supervised Super-Plane for Neural 3D Reconstruction | Botao Ye, Sifei Liu, Xueting Li, Ming-Hsuan Yang | Neural implicit surface representation methods show impressive reconstruction results but struggle to handle texture-less planar regions that widely exist in indoor scenes. Existing approaches addressing this leverage image prior that requires assistive networks trained with large-scale annotated datasets. In this work, we introduce a self-supervised super-plane constraint by exploring the free geometry cues from the predicted surface, which can further regularize the reconstruction of plane regions without any other ground truth annotations. Specifically, we introduce an iterative training scheme, where (i) grouping of pixels to formulate a super-plane (analogous to super-pixels), and (ii) optimizing of the scene reconstruction network via a super-plane constraint, are progressively conducted. We demonstrate that the model trained with super-planes surprisingly outperforms the one using conventional annotated planes, as individual super-plane statistically occupies a larger area and leads to more stable training. Extensive experiments show that our self-supervised super-plane constraint significantly improves 3D reconstruction quality even better than using ground truth plane segmentation. Additionally, the plane reconstruction results from our model can be used for auto-labeling for other vision tasks. The code and models are available at https: //github.com/botaoye/S3PRecon. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ye_Self-Supervised_Super-Plane_for_Neural_3D_Reconstruction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ye_Self-Supervised_Super-Plane_for_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ye_Self-Supervised_Super-Plane_for_Neural_3D_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ye_Self-Supervised_Super-Plane_for_Neural_3D_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
DisCo-CLIP: A Distributed Contrastive Loss for Memory Efficient CLIP Training | Yihao Chen, Xianbiao Qi, Jianan Wang, Lei Zhang | We propose DisCo-CLIP, a distributed memory-efficient CLIP training approach, to reduce the memory consumption of contrastive loss when training contrastive learning models. Our approach decomposes the contrastive loss and its gradient computation into two parts, one to calculate the intra-GPU gradients and the other to compute the inter-GPU gradients. According to our decomposition, only the intra-GPU gradients are computed on the current GPU, while the inter-GPU gradients are collected via all_reduce from other GPUs instead of being repeatedly computed on every GPU. In this way, we can reduce the GPU memory consumption of contrastive loss computation from O(B^2) to O(B^2 / N), where B and N are the batch size and the number of GPUs used for training. Such a distributed solution is mathematically equivalent to the original non-distributed contrastive loss computation, without sacrificing any computation accuracy. It is particularly efficient for large-batch CLIP training. For instance, DisCo-CLIP can enable contrastive training of a ViT-B/32 model with a batch size of 32K or 196K using 8 or 64 A100 40GB GPUs, compared with the original CLIP solution which requires 128 A100 40GB GPUs to train a ViT-B/32 model with a batch size of 32K. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_DisCo-CLIP_A_Distributed_Contrastive_Loss_for_Memory_Efficient_CLIP_Training_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_DisCo-CLIP_A_Distributed_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_DisCo-CLIP_A_Distributed_Contrastive_Loss_for_Memory_Efficient_CLIP_Training_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_DisCo-CLIP_A_Distributed_Contrastive_Loss_for_Memory_Efficient_CLIP_Training_CVPR_2023_paper.html | CVPR 2023 | null |
GM-NeRF: Learning Generalizable Model-Based Neural Radiance Fields From Multi-View Images | Jianchuan Chen, Wentao Yi, Liqian Ma, Xu Jia, Huchuan Lu | In this work, we focus on synthesizing high-fidelity novel view images for arbitrary human performers, given a set of sparse multi-view images. It is a challenging task due to the large variation among articulated body poses and heavy self-occlusions. To alleviate this, we introduce an effective generalizable framework Generalizable Model-based Neural Radiance Fields (GM-NeRF) to synthesize free-viewpoint images. Specifically, we propose a geometry-guided attention mechanism to register the appearance code from multi-view 2D images to a geometry proxy which can alleviate the misalignment between inaccurate geometry prior and pixel space. On top of that, we further conduct neural rendering and partial gradient backpropagation for efficient perceptual supervision and improvement of the perceptual quality of synthesis. To evaluate our method, we conduct experiments on synthesized datasets THuman2.0 and Multi-garment, and real-world datasets Genebody and ZJUMocap. The results demonstrate that our approach outperforms state-of-the-art methods in terms of novel view synthesis and geometric reconstruction. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_GM-NeRF_Learning_Generalizable_Model-Based_Neural_Radiance_Fields_From_Multi-View_Images_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_GM-NeRF_Learning_Generalizable_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_GM-NeRF_Learning_Generalizable_Model-Based_Neural_Radiance_Fields_From_Multi-View_Images_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_GM-NeRF_Learning_Generalizable_Model-Based_Neural_Radiance_Fields_From_Multi-View_Images_CVPR_2023_paper.html | CVPR 2023 | null |
VDN-NeRF: Resolving Shape-Radiance Ambiguity via View-Dependence Normalization | Bingfan Zhu, Yanchao Yang, Xulong Wang, Youyi Zheng, Leonidas Guibas | We propose VDN-NeRF, a method to train neural radiance fields (NeRFs) for better geometry under non-Lambertian surface and dynamic lighting conditions that cause significant variation in the radiance of a point when viewed from different angles. Instead of explicitly modeling the underlying factors that result in the view-dependent phenomenon, which could be complex yet not inclusive, we develop a simple and effective technique that normalizes the view-dependence by distilling invariant information already encoded in the learned NeRFs. We then jointly train NeRFs for view synthesis with view-dependence normalization to attain quality geometry. Our experiments show that even though shape-radiance ambiguity is inevitable, the proposed normalization can minimize its effect on geometry, which essentially aligns the optimal capacity needed for explaining view-dependent variations. Our method applies to various baselines and significantly improves geometry without changing the volume rendering pipeline, even if the data is captured under a moving light source. Code is available at: https://github.com/BoifZ/VDN-NeRF. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_VDN-NeRF_Resolving_Shape-Radiance_Ambiguity_via_View-Dependence_Normalization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhu_VDN-NeRF_Resolving_Shape-Radiance_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_VDN-NeRF_Resolving_Shape-Radiance_Ambiguity_via_View-Dependence_Normalization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_VDN-NeRF_Resolving_Shape-Radiance_Ambiguity_via_View-Dependence_Normalization_CVPR_2023_paper.html | CVPR 2023 | null |
Mobile User Interface Element Detection via Adaptively Prompt Tuning | Zhangxuan Gu, Zhuoer Xu, Haoxing Chen, Jun Lan, Changhua Meng, Weiqiang Wang | Recent object detection approaches rely on pretrained vision-language models for image-text alignment. However, they fail to detect the Mobile User Interface (MUI) element since it contains additional OCR information, which describes its content and function but is often ignored. In this paper, we develop a new MUI element detection dataset named MUI-zh and propose an Adaptively Prompt Tuning (APT) module to take advantage of discriminating OCR information. APT is a lightweight and effective module to jointly optimize category prompts across different modalities. For every element, APT uniformly encodes its visual features and OCR descriptions to dynamically adjust the representation of frozen category prompts. We evaluate the effectiveness of our plug-and-play APT upon several existing CLIP-based detectors for both standard and open-vocabulary MUI element detection. Extensive experiments show that our method achieves considerable improvements on two datasets. The datasets is available at github.com/antmachineintelligence/MUI-zh. | https://openaccess.thecvf.com/content/CVPR2023/papers/Gu_Mobile_User_Interface_Element_Detection_via_Adaptively_Prompt_Tuning_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gu_Mobile_User_Interface_Element_Detection_via_Adaptively_Prompt_Tuning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gu_Mobile_User_Interface_Element_Detection_via_Adaptively_Prompt_Tuning_CVPR_2023_paper.html | CVPR 2023 | null |
Perspective Fields for Single Image Camera Calibration | Linyi Jin, Jianming Zhang, Yannick Hold-Geoffroy, Oliver Wang, Kevin Blackburn-Matzen, Matthew Sticha, David F. Fouhey | Geometric camera calibration is often required for applications that understand the perspective of the image. We propose perspective fields as a representation that models the local perspective properties of an image. Perspective Fields contain per-pixel information about the camera view, parameterized as an up vector and a latitude value. This representation has a number of advantages as it makes minimal assumptions about the camera model and is invariant or equivariant to common image editing operations like cropping, warping, and rotation. It is also more interpretable and aligned with human perception. We train a neural network to predict Perspective Fields and the predicted Perspective Fields can be converted to calibration parameters easily. We demonstrate the robustness of our approach under various scenarios compared with camera calibration-based methods and show example applications in image compositing. Project page: https://jinlinyi.github.io/PerspectiveFields/ | https://openaccess.thecvf.com/content/CVPR2023/papers/Jin_Perspective_Fields_for_Single_Image_Camera_Calibration_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jin_Perspective_Fields_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.03239 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jin_Perspective_Fields_for_Single_Image_Camera_Calibration_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jin_Perspective_Fields_for_Single_Image_Camera_Calibration_CVPR_2023_paper.html | CVPR 2023 | null |
Sparse Multi-Modal Graph Transformer With Shared-Context Processing for Representation Learning of Giga-Pixel Images | Ramin Nakhli, Puria Azadi Moghadam, Haoyang Mi, Hossein Farahani, Alexander Baras, Blake Gilks, Ali Bashashati | Processing giga-pixel whole slide histopathology images (WSI) is a computationally expensive task. Multiple instance learning (MIL) has become the conventional approach to process WSIs, in which these images are split into smaller patches for further processing. However, MIL-based techniques ignore explicit information about the individual cells within a patch. In this paper, by defining the novel concept of shared-context processing, we designed a multi-modal Graph Transformer that uses the cellular graph within the tissue to provide a single representation for a patient while taking advantage of the hierarchical structure of the tissue, enabling a dynamic focus between cell-level and tissue-level information. We benchmarked the performance of our model against multiple state-of-the-art methods in survival prediction and showed that ours can significantly outperform all of them including hierarchical vision Transformer (ViT). More importantly, we show that our model is strongly robust to missing information to an extent that it can achieve the same performance with as low as 20% of the data. Finally, in two different cancer datasets, we demonstrated that our model was able to stratify the patients into low-risk and high-risk groups while other state-of-the-art methods failed to achieve this goal. We also publish a large dataset of immunohistochemistry (IHC) images containing 1,600 tissue microarray (TMA) cores from 188 patients along with their survival information, making it one of the largest publicly available datasets in this context. | https://openaccess.thecvf.com/content/CVPR2023/papers/Nakhli_Sparse_Multi-Modal_Graph_Transformer_With_Shared-Context_Processing_for_Representation_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Nakhli_Sparse_Multi-Modal_Graph_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.00865 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Nakhli_Sparse_Multi-Modal_Graph_Transformer_With_Shared-Context_Processing_for_Representation_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Nakhli_Sparse_Multi-Modal_Graph_Transformer_With_Shared-Context_Processing_for_Representation_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Generating Human Motion From Textual Descriptions With Discrete Representations | Jianrong Zhang, Yangsong Zhang, Xiaodong Cun, Yong Zhang, Hongwei Zhao, Hongtao Lu, Xi Shen, Ying Shan | In this work, we investigate a simple and must-known conditional generative framework based on Vector Quantised-Variational AutoEncoder (VQ-VAE) and Generative Pre-trained Transformer (GPT) for human motion generation from textural descriptions. We show that a simple CNN-based VQ-VAE with commonly used training recipes (EMA and Code Reset) allows us to obtain high-quality discrete representations. For GPT, we incorporate a simple corruption strategy during the training to alleviate training-testing discrepancy. Despite its simplicity, our T2M-GPT shows better performance than competitive approaches, including recent diffusion-based approaches. For example, on HumanML3D, which is currently the largest dataset, we achieve comparable performance on the consistency between text and generated motion (R-Precision), but with FID 0.116 largely outperforming MotionDiffuse of 0.630. Additionally, we conduct analyses on HumanML3D and observe that the dataset size is a limitation of our approach. Our work suggests that VQ-VAE still remains a competitive approach for human motion generation. Our implementation is available on the project page: https://mael-zys.github.io/T2M-GPT/ | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Generating_Human_Motion_From_Textual_Descriptions_With_Discrete_Representations_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Generating_Human_Motion_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.06052 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Generating_Human_Motion_From_Textual_Descriptions_With_Discrete_Representations_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Generating_Human_Motion_From_Textual_Descriptions_With_Discrete_Representations_CVPR_2023_paper.html | CVPR 2023 | null |
Spatial-Temporal Concept Based Explanation of 3D ConvNets | Ying Ji, Yu Wang, Jien Kato | Convolutional neural networks (CNNs) have shown remarkable performance on various tasks. Despite its widespread adoption, the decision procedure of the network still lacks transparency and interpretability, making it difficult to enhance the performance further. Hence, there has been considerable interest in providing explanation and interpretability for CNNs over the last few years. Explainable artificial intelligence (XAI) investigates the relationship between input images or videos and output predictions. Recent studies have achieved outstanding success in explaining 2D image classification ConvNets. On the other hand, due to the high computation cost and complexity of video data, the explanation of 3D video recognition ConvNets is relatively less studied. And none of them are able to produce a high-level explanation. In this paper, we propose a STCE (Spatial-temporal Concept-based Explanation) framework for interpreting 3D ConvNets. In our approach: (1) videos are represented with high-level supervoxels, similar supervoxels are clustered as a concept, which is straightforward for human to understand; and (2) the interpreting framework calculates a score for each concept, which reflects its significance in the ConvNet decision procedure. Experiments on diverse 3D ConvNets demonstrate that our method can identify global concepts with different importance levels, allowing us to investigate the impact of the concepts on a target task, such as action recognition, in-depth. The source codes are publicly available at https://github.com/yingji425/STCE. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ji_Spatial-Temporal_Concept_Based_Explanation_of_3D_ConvNets_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2206.05275 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ji_Spatial-Temporal_Concept_Based_Explanation_of_3D_ConvNets_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ji_Spatial-Temporal_Concept_Based_Explanation_of_3D_ConvNets_CVPR_2023_paper.html | CVPR 2023 | null |
Robust Test-Time Adaptation in Dynamic Scenarios | Longhui Yuan, Binhui Xie, Shuang Li | Test-time adaptation (TTA) intends to adapt the pretrained model to test distributions with only unlabeled test data streams. Most of the previous TTA methods have achieved great success on simple test data streams such as independently sampled data from single or multiple distributions. However, these attempts may fail in dynamic scenarios of real-world applications like autonomous driving, where the environments gradually change and the test data is sampled correlatively over time. In this work, we explore such practical test data streams to deploy the model on the fly, namely practical test-time adaptation (PTTA). To do so, we elaborate a Robust Test-Time Adaptation (RoTTA) method against the complex data stream in PTTA. More specifically, we present a robust batch normalization scheme to estimate the normalization statistics. Meanwhile, a memory bank is utilized to sample category-balanced data with consideration of timeliness and uncertainty. Further, to stabilize the training procedure, we develop a time-aware reweighting strategy with a teacher-student model. Extensive experiments prove that RoTTA enables continual testtime adaptation on the correlatively sampled data streams. Our method is easy to implement, making it a good choice for rapid deployment. The code is publicly available at https://github.com/BIT-DA/RoTTA | https://openaccess.thecvf.com/content/CVPR2023/papers/Yuan_Robust_Test-Time_Adaptation_in_Dynamic_Scenarios_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yuan_Robust_Test-Time_Adaptation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13899 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yuan_Robust_Test-Time_Adaptation_in_Dynamic_Scenarios_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yuan_Robust_Test-Time_Adaptation_in_Dynamic_Scenarios_CVPR_2023_paper.html | CVPR 2023 | null |
Global and Local Mixture Consistency Cumulative Learning for Long-Tailed Visual Recognitions | Fei Du, Peng Yang, Qi Jia, Fengtao Nan, Xiaoting Chen, Yun Yang | In this paper, our goal is to design a simple learning paradigm for long-tail visual recognition, which not only improves the robustness of the feature extractor but also alleviates the bias of the classifier towards head classes while reducing the training skills and overhead. We propose an efficient one-stage training strategy for long-tailed visual recognition called Global and Local Mixture Consistency cumulative learning (GLMC). Our core ideas are twofold: (1) a global and local mixture consistency loss improves the robustness of the feature extractor. Specifically, we generate two augmented batches by the global MixUp and local CutMix from the same batch data, respectively, and then use cosine similarity to minimize the difference. (2) A cumulative head-tail soft label reweighted loss mitigates the head class bias problem. We use empirical class frequencies to reweight the mixed label of the head-tail class for long-tailed data and then balance the conventional loss and the rebalanced loss with a coefficient accumulated by epochs. Our approach achieves state-of-the-art accuracy on CIFAR10-LT, CIFAR100-LT, and ImageNet-LT datasets. Additional experiments on balanced ImageNet and CIFAR demonstrate that GLMC can significantly improve the generalization of backbones. Code is made publicly available at https://github.com/ynu-yangpeng/GLMC | https://openaccess.thecvf.com/content/CVPR2023/papers/Du_Global_and_Local_Mixture_Consistency_Cumulative_Learning_for_Long-Tailed_Visual_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Du_Global_and_Local_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2305.08661 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Du_Global_and_Local_Mixture_Consistency_Cumulative_Learning_for_Long-Tailed_Visual_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Du_Global_and_Local_Mixture_Consistency_Cumulative_Learning_for_Long-Tailed_Visual_CVPR_2023_paper.html | CVPR 2023 | null |
NIRVANA: Neural Implicit Representations of Videos With Adaptive Networks and Autoregressive Patch-Wise Modeling | Shishira R. Maiya, Sharath Girish, Max Ehrlich, Hanyu Wang, Kwot Sin Lee, Patrick Poirson, Pengxiang Wu, Chen Wang, Abhinav Shrivastava | Implicit Neural Representations (INR) have recently shown to be powerful tool for high-quality video compression. However, existing works are limiting as they do not explicitly exploit the temporal redundancy in videos, leading to a long encoding time. Additionally, these methods have fixed architectures which do not scale to longer videos or higher resolutions. To address these issues, we propose NIRVANA, which treats videos as groups of frames and fits separate networks to each group performing patch-wise prediction. %This design shares computation within each group, in the spatial and temporal dimensions, resulting in reduced encoding time of the video. The video representation is modeled autoregressively, with networks fit on a current group initialized using weights from the previous group's model. To further enhance efficiency, we perform quantization of the network parameters during training, requiring no post-hoc pruning or quantization. When compared with previous works on the benchmark UVG dataset, NIRVANA improves encoding quality from 37.36 to 37.70 (in terms of PSNR) and the encoding speed by 12x, while maintaining the same compression rate. In contrast to prior video INR works which struggle with larger resolution and longer videos, we show that our algorithm is highly flexible and scales naturally due to its patch-wise and autoregressive designs. Moreover, our method achieves variable bitrate compression by adapting to videos with varying inter-frame motion. NIRVANA achieves 6x decoding speed and scales well with more GPUs, making it practical for various deployment scenarios. | https://openaccess.thecvf.com/content/CVPR2023/papers/Maiya_NIRVANA_Neural_Implicit_Representations_of_Videos_With_Adaptive_Networks_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Maiya_NIRVANA_Neural_Implicit_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.14593 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Maiya_NIRVANA_Neural_Implicit_Representations_of_Videos_With_Adaptive_Networks_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Maiya_NIRVANA_Neural_Implicit_Representations_of_Videos_With_Adaptive_Networks_and_CVPR_2023_paper.html | CVPR 2023 | null |
Towards Accurate Image Coding: Improved Autoregressive Image Generation With Dynamic Vector Quantization | Mengqi Huang, Zhendong Mao, Zhuowei Chen, Yongdong Zhang | Existing vector quantization (VQ) based autoregressive models follow a two-stage generation paradigm that first learns a codebook to encode images as discrete codes, and then completes generation based on the learned codebook. However, they encode fixed-size image regions into fixed-length codes and ignore their naturally different information densities, which results in insufficiency in important regions and redundancy in unimportant ones, and finally degrades the generation quality and speed. Moreover, the fixed-length coding leads to an unnatural raster-scan autoregressive generation. To address the problem, we propose a novel two-stage framework: (1) Dynamic-Quantization VAE (DQ-VAE) which encodes image regions into variable-length codes based on their information densities for an accurate & compact code representation. (2) DQ-Transformer which thereby generates images autoregressively from coarse-grained (smooth regions with fewer codes) to fine-grained (details regions with more codes) by modeling the position and content of codes in each granularity alternately, through a novel stacked-transformer architecture and shared-content, non-shared position input layers designs. Comprehensive experiments on various generation tasks validate our superiorities in both effectiveness and efficiency. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Towards_Accurate_Image_Coding_Improved_Autoregressive_Image_Generation_With_Dynamic_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_Towards_Accurate_Image_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Towards_Accurate_Image_Coding_Improved_Autoregressive_Image_Generation_With_Dynamic_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Towards_Accurate_Image_Coding_Improved_Autoregressive_Image_Generation_With_Dynamic_CVPR_2023_paper.html | CVPR 2023 | null |
Coaching a Teachable Student | Jimuyang Zhang, Zanming Huang, Eshed Ohn-Bar | We propose a novel knowledge distillation framework for effectively teaching a sensorimotor student agent to drive from the supervision of a privileged teacher agent. Current distillation for sensorimotor agents methods tend to result in suboptimal learned driving behavior by the student, which we hypothesize is due to inherent differences between the input, modeling capacity, and optimization processes of the two agents. We develop a novel distillation scheme that can address these limitations and close the gap between the sensorimotor agent and its privileged teacher. Our key insight is to design a student which learns to align their input features with the teacher's privileged Bird's Eye View (BEV) space. The student then can benefit from direct supervision by the teacher over the internal representation learning. To scaffold the difficult sensorimotor learning task, the student model is optimized via a student-paced coaching mechanism with various auxiliary supervision. We further propose a high-capacity imitation learned privileged agent that surpasses prior privileged agents in CARLA and ensures the student learns safe driving behavior. Our proposed sensorimotor agent results in a robust image-based behavior cloning agent in CARLA, improving over current models by over 20.6% in driving score without requiring LiDAR, historical observations, ensemble of models, on-policy data aggregation or reinforcement learning. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Coaching_a_Teachable_Student_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Coaching_a_Teachable_Student_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Coaching_a_Teachable_Student_CVPR_2023_paper.html | CVPR 2023 | null |
Collaboration Helps Camera Overtake LiDAR in 3D Detection | Yue Hu, Yifan Lu, Runsheng Xu, Weidi Xie, Siheng Chen, Yanfeng Wang | Camera-only 3D detection provides an economical solution with a simple configuration for localizing objects in 3D space compared to LiDAR-based detection systems. However, a major challenge lies in precise depth estimation due to the lack of direct 3D measurements in the input. Many previous methods attempt to improve depth estimation through network designs, e.g., deformable layers and larger receptive fields. This work proposes an orthogonal direction, improving the camera-only 3D detection by introducing multi-agent collaborations. Our proposed collaborative camera-only 3D detection (CoCa3D) enables agents to share complementary information with each other through communication. Meanwhile, we optimize communication efficiency by selecting the most informative cues. The shared messages from multiple viewpoints disambiguate the single-agent estimated depth and complement the occluded and long-range regions in the single-agent view. We evaluate CoCa3D in one real-world dataset and two new simulation datasets. Results show that CoCa3D improves previous SOTA performances by 44.21% on DAIR-V2X, 30.60% on OPV2V+, 12.59% on CoPerception-UAVs+ for AP@70. Our preliminary results show a potential that with sufficient collaboration, the camera might overtake LiDAR in some practical scenarios. We released the dataset and code at https://siheng-chen.github.io/dataset/CoPerception+ and https://github.com/MediaBrain-SJTU/CoCa3D. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hu_Collaboration_Helps_Camera_Overtake_LiDAR_in_3D_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hu_Collaboration_Helps_Camera_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13560 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_Collaboration_Helps_Camera_Overtake_LiDAR_in_3D_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_Collaboration_Helps_Camera_Overtake_LiDAR_in_3D_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
RealImpact: A Dataset of Impact Sound Fields for Real Objects | Samuel Clarke, Ruohan Gao, Mason Wang, Mark Rau, Julia Xu, Jui-Hsien Wang, Doug L. James, Jiajun Wu | Objects make unique sounds under different perturbations, environment conditions, and poses relative to the listener. While prior works have modeled impact sounds and sound propagation in simulation, we lack a standard dataset of impact sound fields of real objects for audio-visual learning and calibration of the sim-to-real gap. We present RealImpact, a large-scale dataset of real object impact sounds recorded under controlled conditions. RealImpact contains 150,000 recordings of impact sounds of 50 everyday objects with detailed annotations, including their impact locations, microphone locations, contact force profiles, material labels, and RGBD images. We make preliminary attempts to use our dataset as a reference to current simulation methods for estimating object impact sounds that match the real world. Moreover, we demonstrate the usefulness of our dataset as a testbed for acoustic and audio-visual learning via the evaluation of two benchmark tasks, including listener location classification and visual acoustic matching. | https://openaccess.thecvf.com/content/CVPR2023/papers/Clarke_RealImpact_A_Dataset_of_Impact_Sound_Fields_for_Real_Objects_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Clarke_RealImpact_A_Dataset_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Clarke_RealImpact_A_Dataset_of_Impact_Sound_Fields_for_Real_Objects_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Clarke_RealImpact_A_Dataset_of_Impact_Sound_Fields_for_Real_Objects_CVPR_2023_paper.html | CVPR 2023 | null |
ReCo: Region-Controlled Text-to-Image Generation | Zhengyuan Yang, Jianfeng Wang, Zhe Gan, Linjie Li, Kevin Lin, Chenfei Wu, Nan Duan, Zicheng Liu, Ce Liu, Michael Zeng, Lijuan Wang | Recently, large-scale text-to-image (T2I) models have shown impressive performance in generating high-fidelity images, but with limited controllability, e.g., precisely specifying the content in a specific region with a free-form text description. In this paper, we propose an effective technique for such regional control in T2I generation. We augment T2I models' inputs with an extra set of position tokens, which represent the quantized spatial coordinates. Each region is specified by four position tokens to represent the top-left and bottom-right corners, followed by an open-ended natural language regional description. Then, we fine-tune a pre-trained T2I model with such new input interface. Our model, dubbed as ReCo (Region-Controlled T2I), enables the region control for arbitrary objects described by open-ended regional texts rather than by object labels from a constrained category set. Empirically, ReCo achieves better image quality than the T2I model strengthened by positional words (FID: 8.82 -> 7.36, SceneFID: 15.54 -> 6.51 on COCO), together with objects being more accurately placed, amounting to a 20.40% region classification accuracy improvement on COCO. Furthermore, we demonstrate that ReCo can better control the object count, spatial relationship, and region attributes such as color/size, with the free-form regional description. Human evaluation on PaintSkill shows that ReCo is +19.28% and +17.21% more accurate in generating images with correct object count and spatial relationship than the T2I model. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_ReCo_Region-Controlled_Text-to-Image_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_ReCo_Region-Controlled_Text-to-Image_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.15518 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_ReCo_Region-Controlled_Text-to-Image_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_ReCo_Region-Controlled_Text-to-Image_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
WINNER: Weakly-Supervised hIerarchical decompositioN and aligNment for Spatio-tEmporal Video gRounding | Mengze Li, Han Wang, Wenqiao Zhang, Jiaxu Miao, Zhou Zhao, Shengyu Zhang, Wei Ji, Fei Wu | Spatio-temporal video grounding aims to localize the aligned visual tube corresponding to a language query. Existing techniques achieve such alignment by exploiting dense boundary and bounding box annotations, which can be prohibitively expensive. To bridge the gap, we investigate the weakly-supervised setting, where models learn from easily accessible video-language data without annotations. We identify that intra-sample spurious correlations among video-language components can be alleviated if the model captures the decomposed structures of video and language data. In this light, we propose a novel framework, namely WINNER, for hierarchical video-text understanding. WINNER first builds the language decomposition tree in a bottom-up manner, upon which the structural attention mechanism and top-down feature backtracking jointly build a multi-modal decomposition tree, permitting a hierarchical understanding of unstructured videos. The multi-modal decomposition tree serves as the basis for multi-hierarchy language-tube matching. A hierarchical contrastive learning objective is proposed to learn the multi-hierarchy correspondence and distinguishment with intra-sample and inter-sample video-text decomposition structures, achieving video-language decomposition structure alignment. Extensive experiments demonstrate the rationality of our design and its effectiveness beyond state-of-the-art weakly supervised methods, even some supervised methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_WINNER_Weakly-Supervised_hIerarchical_decompositioN_and_aligNment_for_Spatio-tEmporal_Video_gRounding_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_WINNER_Weakly-Supervised_hIerarchical_decompositioN_and_aligNment_for_Spatio-tEmporal_Video_gRounding_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_WINNER_Weakly-Supervised_hIerarchical_decompositioN_and_aligNment_for_Spatio-tEmporal_Video_gRounding_CVPR_2023_paper.html | CVPR 2023 | null |
Preserving Linear Separability in Continual Learning by Backward Feature Projection | Qiao Gu, Dongsub Shim, Florian Shkurti | Catastrophic forgetting has been a major challenge in continual learning, where the model needs to learn new tasks with limited or no access to data from previously seen tasks. To tackle this challenge, methods based on knowledge distillation in feature space have been proposed and shown to reduce forgetting. However, most feature distillation methods directly constrain the new features to match the old ones, overlooking the need for plasticity. To achieve a better stability-plasticity trade-off, we propose Backward Feature Projection (BFP), a method for continual learning that allows the new features to change up to a learnable linear transformation of the old features. BFP preserves the linear separability of the old classes while allowing the emergence of new feature directions to accommodate new classes. BFP can be integrated with existing experience replay methods and boost performance by a significant margin. We also demonstrate that BFP helps learn a better representation space, in which linear separability is well preserved during continual learning and linear probing achieves high classification accuracy. | https://openaccess.thecvf.com/content/CVPR2023/papers/Gu_Preserving_Linear_Separability_in_Continual_Learning_by_Backward_Feature_Projection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gu_Preserving_Linear_Separability_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14595 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gu_Preserving_Linear_Separability_in_Continual_Learning_by_Backward_Feature_Projection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gu_Preserving_Linear_Separability_in_Continual_Learning_by_Backward_Feature_Projection_CVPR_2023_paper.html | CVPR 2023 | null |
MHPL: Minimum Happy Points Learning for Active Source Free Domain Adaptation | Fan Wang, Zhongyi Han, Zhiyan Zhang, Rundong He, Yilong Yin | Source free domain adaptation (SFDA) aims to transfer a trained source model to the unlabeled target domain without accessing the source data. However, the SFDA setting faces a performance bottleneck due to the absence of source data and target supervised information, as evidenced by the limited performance gains of the newest SFDA methods. Active source free domain adaptation (ASFDA) can break through the problem by exploring and exploiting a small set of informative samples via active learning. In this paper, we first find that those satisfying the properties of neighbor-chaotic, individual-different, and source-dissimilar are the best points to select. We define them as the minimum happy (MH) points challenging to explore with existing methods. We propose minimum happy points learning (MHPL) to explore and exploit MH points actively. We design three unique strategies: neighbor environment uncertainty, neighbor diversity relaxation, and one-shot querying, to explore the MH points. Further, to fully exploit MH points in the learning process, we design a neighbor focal loss that assigns the weighted neighbor purity to the cross entropy loss of MH points to make the model focus more on them. Extensive experiments verify that MHPL remarkably exceeds the various types of baselines and achieves significant performance gains at a small cost of labeling. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_MHPL_Minimum_Happy_Points_Learning_for_Active_Source_Free_Domain_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_MHPL_Minimum_Happy_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_MHPL_Minimum_Happy_Points_Learning_for_Active_Source_Free_Domain_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_MHPL_Minimum_Happy_Points_Learning_for_Active_Source_Free_Domain_CVPR_2023_paper.html | CVPR 2023 | null |
Fix the Noise: Disentangling Source Feature for Controllable Domain Translation | Dongyeun Lee, Jae Young Lee, Doyeon Kim, Jaehyun Choi, Jaejun Yoo, Junmo Kim | Recent studies show strong generative performance in domain translation especially by using transfer learning techniques on the unconditional generator. However, the control between different domain features using a single model is still challenging. Existing methods often require additional models, which is computationally demanding and leads to unsatisfactory visual quality. In addition, they have restricted control steps, which prevents a smooth transition. In this paper, we propose a new approach for high-quality domain translation with better controllability. The key idea is to preserve source features within a disentangled subspace of a target feature space. This allows our method to smoothly control the degree to which it preserves source features while generating images from an entirely new domain using only a single model. Our extensive experiments show that the proposed method can produce more consistent and realistic images than previous works and maintain precise controllability over different levels of transformation. The code is available at LeeDongYeun/FixNoise. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lee_Fix_the_Noise_Disentangling_Source_Feature_for_Controllable_Domain_Translation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lee_Fix_the_Noise_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11545 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_Fix_the_Noise_Disentangling_Source_Feature_for_Controllable_Domain_Translation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_Fix_the_Noise_Disentangling_Source_Feature_for_Controllable_Domain_Translation_CVPR_2023_paper.html | CVPR 2023 | null |
Metadata-Based RAW Reconstruction via Implicit Neural Functions | Leyi Li, Huijie Qiao, Qi Ye, Qinmin Yang | Many low-level computer vision tasks are desirable to utilize the unprocessed RAW image as input, which remains the linear relationship between pixel values and scene radiance. Recent works advocate to embed the RAW image samples into sRGB images at capture time, and reconstruct the RAW from sRGB by these metadata when needed. However, there still exist some limitations on taking full use of the metadata. In this paper, instead of following the perspective of sRGB-to-RAW mapping, we reformulate the problem as mapping the 2D coordinates of the metadata to its RAW values conditioned on the corresponding sRGB values. With this novel formulation, we propose to reconstruct the RAW image with an implicit neural function, which achieves significant performance improvement (more than 10dB average PSNR) only with the uniform sampling. Compared with most deep learning-based approaches, our method is trained in a self-supervised way that requiring no pre-training on different camera ISPs. We perform further experiments to demonstrate the effectiveness of our method, and show that our framework is also suitable for the task of guided super-resolution. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Metadata-Based_RAW_Reconstruction_via_Implicit_Neural_Functions_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Metadata-Based_RAW_Reconstruction_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Metadata-Based_RAW_Reconstruction_via_Implicit_Neural_Functions_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Metadata-Based_RAW_Reconstruction_via_Implicit_Neural_Functions_CVPR_2023_paper.html | CVPR 2023 | null |
Uni-Perceiver v2: A Generalist Model for Large-Scale Vision and Vision-Language Tasks | Hao Li, Jinguo Zhu, Xiaohu Jiang, Xizhou Zhu, Hongsheng Li, Chun Yuan, Xiaohua Wang, Yu Qiao, Xiaogang Wang, Wenhai Wang, Jifeng Dai | Despite the remarkable success of foundation models, their task-specific fine-tuning paradigm makes them inconsistent with the goal of general perception modeling. The key to eliminating this inconsistency is to use generalist models for general task modeling. However, existing attempts at generalist models are inadequate in both versatility and performance. In this paper, we propose Uni-Perceiver v2, which is the first generalist model capable of handling major large-scale vision and vision-language tasks with competitive performance. Specifically, images are encoded as general region proposals, while texts are encoded via a Transformer-based language model. The encoded representations are transformed by a task-agnostic decoder. Different tasks are formulated as a unified maximum likelihood estimation problem. We further propose an effective optimization technique named Task-Balanced Gradient Normalization to ensure stable multi-task learning with an unmixed sampling strategy, which is helpful for tasks requiring large batch-size training. After being jointly trained on various tasks, Uni-Perceiver v2 is capable of directly handling downstream tasks without any task-specific adaptation. Results show that Uni-Perceiver v2 outperforms all existing generalist models in both versatility and performance. Meanwhile, compared with the commonly-recognized strong baselines that require tasks-specific fine-tuning, Uni-Perceiver v2 achieves competitive performance on a broad range of vision and vision-language tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Uni-Perceiver_v2_A_Generalist_Model_for_Large-Scale_Vision_and_Vision-Language_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Uni-Perceiver_v2_A_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.09808 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Uni-Perceiver_v2_A_Generalist_Model_for_Large-Scale_Vision_and_Vision-Language_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Uni-Perceiver_v2_A_Generalist_Model_for_Large-Scale_Vision_and_Vision-Language_CVPR_2023_paper.html | CVPR 2023 | null |
Sparsely Annotated Semantic Segmentation With Adaptive Gaussian Mixtures | Linshan Wu, Zhun Zhong, Leyuan Fang, Xingxin He, Qiang Liu, Jiayi Ma, Hao Chen | Sparsely annotated semantic segmentation (SASS) aims to learn a segmentation model by images with sparse labels (i.e., points or scribbles). Existing methods mainly focus on introducing low-level affinity or generating pseudo labels to strengthen supervision, while largely ignoring the inherent relation between labeled and unlabeled pixels. In this paper, we observe that pixels that are close to each other in the feature space are more likely to share the same class. Inspired by this, we propose a novel SASS framework, which is equipped with an Adaptive Gaussian Mixture Model (AGMM). Our AGMM can effectively endow reliable supervision for unlabeled pixels based on the distributions of labeled and unlabeled pixels. Specifically, we first build Gaussian mixtures using labeled pixels and their relatively similar unlabeled pixels, where the labeled pixels act as centroids, for modeling the feature distribution of each class. Then, we leverage the reliable information from labeled pixels and adaptively generated GMM predictions to supervise the training of unlabeled pixels, achieving online, dynamic, and robust self-supervision. In addition, by capturing category-wise Gaussian mixtures, AGMM encourages the model to learn discriminative class decision boundaries in an end-to-end contrastive learning manner. Experimental results conducted on the PASCAL VOC 2012 and Cityscapes datasets demonstrate that our AGMM can establish new state-of-the-art SASS performance. Code is available at https://github.com/Luffy03/AGMM-SASS. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_Sparsely_Annotated_Semantic_Segmentation_With_Adaptive_Gaussian_Mixtures_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_Sparsely_Annotated_Semantic_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Sparsely_Annotated_Semantic_Segmentation_With_Adaptive_Gaussian_Mixtures_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Sparsely_Annotated_Semantic_Segmentation_With_Adaptive_Gaussian_Mixtures_CVPR_2023_paper.html | CVPR 2023 | null |
Multimodality Helps Unimodality: Cross-Modal Few-Shot Learning With Multimodal Models | Zhiqiu Lin, Samuel Yu, Zhiyi Kuang, Deepak Pathak, Deva Ramanan | The ability to quickly learn a new task with minimal instruction - known as few-shot learning - is a central aspect of intelligent agents. Classical few-shot benchmarks make use of few-shot samples from a single modality, but such samples may not be sufficient to characterize an entire concept class. In contrast, humans use cross-modal information to learn new concepts efficiently. In this work, we demonstrate that one can indeed build a better visual dog classifier by reading about dogs and listening to them bark. To do so, we exploit the fact that recent multimodal foundation models such as CLIP are inherently cross-modal, mapping different modalities to the same representation space. Specifically, we propose a simple cross-modal adaptation approach that learns from few-shot examples spanning different modalities. By repurposing class names as additional one-shot training samples, we achieve SOTA results with an embarrassingly simple linear classifier for vision-language adaptation. Furthermore, we show that our approach can benefit existing methods such as prefix tuning and classifier ensembling. Finally, to explore other modalities beyond vision and language, we construct the first (to our knowledge) audiovisual few-shot benchmark and use cross-modal training to improve the performance of both image and audio classification. We hope our success can inspire future works to embrace cross-modality for even broader domains and tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lin_Multimodality_Helps_Unimodality_Cross-Modal_Few-Shot_Learning_With_Multimodal_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lin_Multimodality_Helps_Unimodality_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2301.06267 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Multimodality_Helps_Unimodality_Cross-Modal_Few-Shot_Learning_With_Multimodal_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Multimodality_Helps_Unimodality_Cross-Modal_Few-Shot_Learning_With_Multimodal_Models_CVPR_2023_paper.html | CVPR 2023 | null |
Decompose More and Aggregate Better: Two Closer Looks at Frequency Representation Learning for Human Motion Prediction | Xuehao Gao, Shaoyi Du, Yang Wu, Yang Yang | Encouraged by the effectiveness of encoding temporal dynamics within the frequency domain, recent human motion prediction systems prefer to first convert the motion representation from the original pose space into the frequency space. In this paper, we introduce two closer looks at effective frequency representation learning for robust motion prediction and summarize them as: decompose more and aggregate better. Motivated by these two insights, we develop two powerful units that factorize the frequency representation learning task with a novel decomposition-aggregation two-stage strategy: (1) frequency decomposition unit unweaves multi-view frequency representations from an input body motion by embedding its frequency features into multiple spaces; (2) feature aggregation unit deploys a series of intra-space and inter-space feature aggregation layers to collect comprehensive frequency representations from these spaces for robust human motion prediction. As evaluated on large-scale datasets, we develop a strong baseline model for the human motion prediction task that outperforms state-of-the-art methods by large margins: 8% 12% on Human3.6M, 3% 7% on CMU MoCap, and 7% 10% on 3DPW. | https://openaccess.thecvf.com/content/CVPR2023/papers/Gao_Decompose_More_and_Aggregate_Better_Two_Closer_Looks_at_Frequency_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gao_Decompose_More_and_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_Decompose_More_and_Aggregate_Better_Two_Closer_Looks_at_Frequency_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_Decompose_More_and_Aggregate_Better_Two_Closer_Looks_at_Frequency_CVPR_2023_paper.html | CVPR 2023 | null |
Diversity-Aware Meta Visual Prompting | Qidong Huang, Xiaoyi Dong, Dongdong Chen, Weiming Zhang, Feifei Wang, Gang Hua, Nenghai Yu | We present Diversity-Aware Meta Visual Prompting (DAM-VP), an efficient and effective prompting method for transferring pre-trained models to downstream tasks with frozen backbone. A challenging issue in visual prompting is that image datasets sometimes have a large data diversity whereas a per-dataset generic prompt can hardly handle the complex distribution shift toward the original pretraining data distribution properly. To address this issue, we propose a dataset Diversity-Aware prompting strategy whose initialization is realized by a Meta-prompt. Specifically, we cluster the downstream dataset into small homogeneity subsets in a diversity-adaptive way, with each subset has its own prompt optimized separately. Such a divide-and-conquer design reduces the optimization difficulty greatly and significantly boosts the prompting performance. Furthermore, all the prompts are initialized with a meta-prompt, which is learned across several datasets. It is a bootstrapped paradigm, with the key observation that the prompting knowledge learned from previous datasets could help the prompt to converge faster and perform better on a new dataset. During inference, we dynamically select a proper prompt for each input, based on the feature distance between the input and each subset. Through extensive experiments, our DAM-VP demonstrates superior efficiency and effectiveness, clearly surpassing previous prompting methods in a series of downstream datasets for different pretraining models. Our code is available at: https://github.com/shikiw/DAM-VP. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Diversity-Aware_Meta_Visual_Prompting_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_Diversity-Aware_Meta_Visual_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.08138 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Diversity-Aware_Meta_Visual_Prompting_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Diversity-Aware_Meta_Visual_Prompting_CVPR_2023_paper.html | CVPR 2023 | null |
Affection: Learning Affective Explanations for Real-World Visual Data | Panos Achlioptas, Maks Ovsjanikov, Leonidas Guibas, Sergey Tulyakov | In this work, we explore the space of emotional reactions induced by real-world images. For this, we first introduce a large-scale dataset that contains both categorical emotional reactions and free-form textual explanations for 85,007 publicly available images, analyzed by 6,283 annotators who were asked to indicate and explain how and why they felt when observing a particular image, with a total of 526,749 responses. Although emotional reactions are subjective and sensitive to context (personal mood, social status, past experiences) -- we show that there is significant common ground to capture emotional responses with a large support in the subject population. In light of this observation, we ask the following questions: i) Can we develop neural networks that provide plausible affective responses to real-world visual data explained with language? ii) Can we steer such methods towards producing explanations with varying degrees of pragmatic language, justifying different emotional reactions by grounding them in the visual stimulus? Finally, iii) How to evaluate the performance of such methods for this novel task? In this work, we take the first steps in addressing all of these questions, paving the way for more human-centric and emotionally-aware image analysis systems. Our code and data are publicly available at https://affective-explanations.org. | https://openaccess.thecvf.com/content/CVPR2023/papers/Achlioptas_Affection_Learning_Affective_Explanations_for_Real-World_Visual_Data_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2210.01946 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Achlioptas_Affection_Learning_Affective_Explanations_for_Real-World_Visual_Data_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Achlioptas_Affection_Learning_Affective_Explanations_for_Real-World_Visual_Data_CVPR_2023_paper.html | CVPR 2023 | null |
3D Highlighter: Localizing Regions on 3D Shapes via Text Descriptions | Dale Decatur, Itai Lang, Rana Hanocka | We present 3D Highlighter, a technique for localizing semantic regions on a mesh using text as input. A key feature of our system is the ability to interpret "out-of-domain" localizations. Our system demonstrates the ability to reason about where to place non-obviously related concepts on an input 3D shape, such as adding clothing to a bare 3D animal model. Our method contextualizes the text description using a neural field and colors the corresponding region of the shape using a probability-weighted blend. Our neural optimization is guided by a pre-trained CLIP encoder, which bypasses the need for any 3D datasets or 3D annotations. Thus, 3D Highlighter is highly flexible, general, and capable of producing localizations on a myriad of input shapes. | https://openaccess.thecvf.com/content/CVPR2023/papers/Decatur_3D_Highlighter_Localizing_Regions_on_3D_Shapes_via_Text_Descriptions_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Decatur_3D_Highlighter_Localizing_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.11263 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Decatur_3D_Highlighter_Localizing_Regions_on_3D_Shapes_via_Text_Descriptions_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Decatur_3D_Highlighter_Localizing_Regions_on_3D_Shapes_via_Text_Descriptions_CVPR_2023_paper.html | CVPR 2023 | null |
Iterative Geometry Encoding Volume for Stereo Matching | Gangwei Xu, Xianqi Wang, Xiaohuan Ding, Xin Yang | Recurrent All-Pairs Field Transforms (RAFT) has shown great potentials in matching tasks. However, all-pairs correlations lack non-local geometry knowledge and have difficulties tackling local ambiguities in ill-posed regions. In this paper, we propose Iterative Geometry Encoding Volume (IGEV-Stereo), a new deep network architecture for stereo matching. The proposed IGEV-Stereo builds a combined geometry encoding volume that encodes geometry and context information as well as local matching details, and iteratively indexes it to update the disparity map. To speed up the convergence, we exploit GEV to regress an accurate starting point for ConvGRUs iterations. Our IGEV-Stereo ranks first on KITTI 2015 and 2012 (Reflective) among all published methods and is the fastest among the top 10 methods. In addition, IGEV-Stereo has strong cross-dataset generalization as well as high inference efficiency. We also extend our IGEV to multi-view stereo (MVS), i.e. IGEV-MVS, which achieves competitive accuracy on DTU benchmark. Code is available at https://github.com/gangweiX/IGEV. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Iterative_Geometry_Encoding_Volume_for_Stereo_Matching_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.06615 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Iterative_Geometry_Encoding_Volume_for_Stereo_Matching_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Iterative_Geometry_Encoding_Volume_for_Stereo_Matching_CVPR_2023_paper.html | CVPR 2023 | null |
PLA: Language-Driven Open-Vocabulary 3D Scene Understanding | Runyu Ding, Jihan Yang, Chuhui Xue, Wenqing Zhang, Song Bai, Xiaojuan Qi | Open-vocabulary scene understanding aims to localize and recognize unseen categories beyond the annotated label space. The recent breakthrough of 2D open-vocabulary perception is largely driven by Internet-scale paired image-text data with rich vocabulary concepts. However, this success cannot be directly transferred to 3D scenarios due to the inaccessibility of large-scale 3D-text pairs. To this end, we propose to distill knowledge encoded in pre-trained vision-language (VL) foundation models through captioning multi-view images from 3D, which allows explicitly associating 3D and semantic-rich captions. Further, to foster coarse-to-fine visual-semantic representation learning from captions, we design hierarchical 3D-caption pairs, leveraging geometric constraints between 3D scenes and multi-view images. Finally, by employing contrastive learning, the model learns language-aware embeddings that connect 3D and text for open-vocabulary tasks. Our method not only remarkably outperforms baseline methods by 25.8% 44.7% hIoU and 14.5% 50.4% hAP_ 50 in open-vocabulary semantic and instance segmentation, but also shows robust transferability on challenging zero-shot domain transfer tasks. See the project website at https://dingry.github.io/projects/PLA. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ding_PLA_Language-Driven_Open-Vocabulary_3D_Scene_Understanding_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ding_PLA_Language-Driven_Open-Vocabulary_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.16312 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ding_PLA_Language-Driven_Open-Vocabulary_3D_Scene_Understanding_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ding_PLA_Language-Driven_Open-Vocabulary_3D_Scene_Understanding_CVPR_2023_paper.html | CVPR 2023 | null |
FaceLit: Neural 3D Relightable Faces | Anurag Ranjan, Kwang Moo Yi, Jen-Hao Rick Chang, Oncel Tuzel | We propose a generative framework, FaceLit, capable of generating a 3D face that can be rendered at various user-defined lighting conditions and views, learned purely from 2D images in-the-wild without any manual annotation. Unlike existing works that require careful capture setup or human labor, we rely on off-the-shelf pose and illumination estimators. With these estimates, we incorporate the Phong reflectance model in the neural volume rendering framework. Our model learns to generate shape and material properties of a face such that, when rendered according to the natural statistics of pose and illumination, produces photorealistic face images with multiview 3D and illumination consistency. Our method enables photorealistic generation of faces with explicit illumination and view controls on multiple datasets -- FFHQ, MetFaces and CelebA-HQ. We show state-of-the-art photorealism among 3D aware GANs on FFHQ dataset achieving an FID score of 3.5. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ranjan_FaceLit_Neural_3D_Relightable_Faces_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ranjan_FaceLit_Neural_3D_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.15437 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ranjan_FaceLit_Neural_3D_Relightable_Faces_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ranjan_FaceLit_Neural_3D_Relightable_Faces_CVPR_2023_paper.html | CVPR 2023 | null |
Visual Programming: Compositional Visual Reasoning Without Training | Tanmay Gupta, Aniruddha Kembhavi | We present VISPROG, a neuro-symbolic approach to solving complex and compositional visual tasks given natural language instructions. VISPROG avoids the need for any task-specific training. Instead, it uses the in-context learning ability of large language models to generate python-like modular programs, which are then executed to get both the solution and a comprehensive and interpretable rationale. Each line of the generated program may invoke one of several off-the-shelf computer vision models, image processing routines, or python functions to produce intermediate outputs that may be consumed by subsequent parts of the program. We demonstrate the flexibility of VISPROG on 4 diverse tasks - compositional visual question answering, zero-shot reasoning on image pairs, factual knowledge object tagging, and language-guided image editing. We believe neuro-symbolic approaches like VISPROG are an exciting avenue to easily and effectively expand the scope of AI systems to serve the long tail of complex tasks that people may wish to perform. | https://openaccess.thecvf.com/content/CVPR2023/papers/Gupta_Visual_Programming_Compositional_Visual_Reasoning_Without_Training_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gupta_Visual_Programming_Compositional_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2211.11559 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gupta_Visual_Programming_Compositional_Visual_Reasoning_Without_Training_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gupta_Visual_Programming_Compositional_Visual_Reasoning_Without_Training_CVPR_2023_paper.html | CVPR 2023 | null |
InstMove: Instance Motion for Object-Centric Video Segmentation | Qihao Liu, Junfeng Wu, Yi Jiang, Xiang Bai, Alan L. Yuille, Song Bai | Despite significant efforts, cutting-edge video segmentation methods still remain sensitive to occlusion and rapid movement, due to their reliance on the appearance of objects in the form of object embeddings, which are vulnerable to these disturbances. A common solution is to use optical flow to provide motion information, but essentially it only considers pixel-level motion, which still relies on appearance similarity and hence is often inaccurate under occlusion and fast movement. In this work, we study the instance-level motion and present InstMove, which stands for Instance Motion for Object-centric Video Segmentation. In comparison to pixel-wise motion, InstMove mainly relies on instance-level motion information that is free from image feature embeddings, and features physical interpretations, making it more accurate and robust toward occlusion and fast-moving objects. To better fit in with the video segmentation tasks, InstMove uses instance masks to model the physical presence of an object and learns the dynamic model through a memory network to predict its position and shape in the next frame. With only a few lines of code, InstMove can be integrated into current SOTA methods for three different video segmentation tasks and boost their performance. Specifically, we improve the previous arts by 1.5 AP on OVIS dataset, which features heavy occlusions, and 4.9 AP on YouTubeVIS-Long dataset, which mainly contains fast-moving objects. These results suggest that instance-level motion is robust and accurate, and hence serving as a powerful solution in complex scenarios for object-centric video segmentation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_InstMove_Instance_Motion_for_Object-Centric_Video_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_InstMove_Instance_Motion_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.08132 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_InstMove_Instance_Motion_for_Object-Centric_Video_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_InstMove_Instance_Motion_for_Object-Centric_Video_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Real-Time Evaluation in Online Continual Learning: A New Hope | Yasir Ghunaim, Adel Bibi, Kumail Alhamoud, Motasem Alfarra, Hasan Abed Al Kader Hammoud, Ameya Prabhu, Philip H.S. Torr, Bernard Ghanem | Current evaluations of Continual Learning (CL) methods typically assume that there is no constraint on training time and computation. This is an unrealistic assumption for any real-world setting, which motivates us to propose: a practical real-time evaluation of continual learning, in which the stream does not wait for the model to complete training before revealing the next data for predictions. To do this, we evaluate current CL methods with respect to their computational costs. We conduct extensive experiments on CLOC, a large-scale dataset containing 39 million time-stamped images with geolocation labels. We show that a simple baseline outperforms state-of-the-art CL methods under this evaluation, questioning the applicability of existing methods in realistic settings. In addition, we explore various CL components commonly used in the literature, including memory sampling strategies and regularization approaches. We find that all considered methods fail to be competitive against our simple baseline. This surprisingly suggests that the majority of existing CL literature is tailored to a specific class of streams that is not practical. We hope that the evaluation we provide will be the first step towards a paradigm shift to consider the computational cost in the development of online continual learning methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ghunaim_Real-Time_Evaluation_in_Online_Continual_Learning_A_New_Hope_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ghunaim_Real-Time_Evaluation_in_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.01047 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ghunaim_Real-Time_Evaluation_in_Online_Continual_Learning_A_New_Hope_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ghunaim_Real-Time_Evaluation_in_Online_Continual_Learning_A_New_Hope_CVPR_2023_paper.html | CVPR 2023 | null |
GRES: Generalized Referring Expression Segmentation | Chang Liu, Henghui Ding, Xudong Jiang | Referring Expression Segmentation (RES) aims to generate a segmentation mask for the object described by a given language expression. Existing classic RES datasets and methods commonly support single-target expressions only, i.e., one expression refers to one target object. Multi-target and no-target expressions are not considered. This limits the usage of RES in practice. In this paper, we introduce a new benchmark called Generalized Referring Expression Segmentation (GRES), which extends the classic RES to allow expressions to refer to an arbitrary number of target objects. Towards this, we construct the first large-scale GRES dataset called gRefCOCO that contains multi-target, no-target, and single-target expressions. GRES and gRefCOCO are designed to be well-compatible with RES, facilitating extensive experiments to study the performance gap of the existing RES methods on the GRES task. In the experimental study, we find that one of the big challenges of GRES is complex relationship modeling. Based on this, we propose a region-based GRES baseline ReLA that adaptively divides the image into regions with sub-instance clues, and explicitly models the region-region and region-language dependencies. The proposed approach ReLA achieves new state-of-the-art performance on the both newly proposed GRES and classic RES tasks. The proposed gRefCOCO dataset and method are available at https://henghuiding.github.io/GRES. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_GRES_Generalized_Referring_Expression_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_GRES_Generalized_Referring_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_GRES_Generalized_Referring_Expression_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_GRES_Generalized_Referring_Expression_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognition | Xiao Yang, Chang Liu, Longlong Xu, Yikai Wang, Yinpeng Dong, Ning Chen, Hang Su, Jun Zhu | Face recognition is a prevailing authentication solution in numerous biometric applications. Physical adversarial attacks, as an important surrogate, can identify the weaknesses of face recognition systems and evaluate their robustness before deployed. However, most existing physical attacks are either detectable readily or ineffective against commercial recognition systems. The goal of this work is to develop a more reliable technique that can carry out an end-to-end evaluation of adversarial robustness for commercial systems. It requires that this technique can simultaneously deceive black-box recognition models and evade defensive mechanisms. To fulfill this, we design adversarial textured 3D meshes (AT3D) with an elaborate topology on a human face, which can be 3D-printed and pasted on the attacker's face to evade the defenses. However, the mesh-based optimization regime calculates gradients in high-dimensional mesh space, and can be trapped into local optima with unsatisfactory transferability. To deviate from the mesh-based space, we propose to perturb the low-dimensional coefficient space based on 3D Morphable Model, which significantly improves black-box transferability meanwhile enjoying faster search efficiency and better visual quality. Extensive experiments in digital and physical scenarios show that our method effectively explores the security vulnerabilities of multiple popular commercial services, including three recognition APIs, four anti-spoofing APIs, two prevailing mobile phones and two automated access control systems. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Towards_Effective_Adversarial_Textured_3D_Meshes_on_Physical_Face_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_Towards_Effective_Adversarial_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15818 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Towards_Effective_Adversarial_Textured_3D_Meshes_on_Physical_Face_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Towards_Effective_Adversarial_Textured_3D_Meshes_on_Physical_Face_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
BAAM: Monocular 3D Pose and Shape Reconstruction With Bi-Contextual Attention Module and Attention-Guided Modeling | Hyo-Jun Lee, Hanul Kim, Su-Min Choi, Seong-Gyun Jeong, Yeong Jun Koh | 3D traffic scene comprises various 3D information about car objects, including their pose and shape. However, most recent studies pay relatively less attention to reconstructing detailed shapes. Furthermore, most of them treat each 3D object as an independent one, resulting in losses of relative context inter-objects and scene context reflecting road circumstances. A novel monocular 3D pose and shape reconstruction algorithm, based on bi-contextual attention and attention-guided modeling (BAAM), is proposed in this work. First, given 2D primitives, we reconstruct 3D object shape based on attention-guided modeling that considers the relevance between detected objects and vehicle shape priors. Next, we estimate 3D object pose through bi-contextual attention, which leverages relation-context inter objects and scene-context between an object and road environment. Finally, we propose a 3D non maximum suppression algorithm to eliminate spurious objects based on their Bird-Eye-View distance. Extensive experiments demonstrate that the proposed BAAM yields state-of-the-art performance on ApolloCar3D. Also, they show that the proposed BAAM can be plugged into any mature monocular 3D object detector on KITTI and significantly boost their performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lee_BAAM_Monocular_3D_Pose_and_Shape_Reconstruction_With_Bi-Contextual_Attention_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lee_BAAM_Monocular_3D_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_BAAM_Monocular_3D_Pose_and_Shape_Reconstruction_With_Bi-Contextual_Attention_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_BAAM_Monocular_3D_Pose_and_Shape_Reconstruction_With_Bi-Contextual_Attention_CVPR_2023_paper.html | CVPR 2023 | null |
Freestyle Layout-to-Image Synthesis | Han Xue, Zhiwu Huang, Qianru Sun, Li Song, Wenjun Zhang | Typical layout-to-image synthesis (LIS) models generate images for a closed set of semantic classes, e.g., 182 common objects in COCO-Stuff. In this work, we explore the freestyle capability of the model, i.e., how far can it generate unseen semantics (e.g., classes, attributes, and styles) onto a given layout, and call the task Freestyle LIS (FLIS). Thanks to the development of large-scale pre-trained language-image models, a number of discriminative models (e.g., image classification and object detection) trained on limited base classes are empowered with the ability of unseen class prediction. Inspired by this, we opt to leverage large-scale pre-trained text-to-image diffusion models to achieve the generation of unseen semantics. The key challenge of FLIS is how to enable the diffusion model to synthesize images from a specific layout which very likely violates its pre-learned knowledge, e.g., the model never sees "a unicorn sitting on a bench" during its pre-training. To this end, we introduce a new module called Rectified Cross-Attention (RCA) that can be conveniently plugged in the diffusion model to integrate semantic masks. This "plug-in" is applied in each cross-attention layer of the model to rectify the attention maps between image and text tokens. The key idea of RCA is to enforce each text token to act on the pixels in a specified region, allowing us to freely put a wide variety of semantics from pre-trained knowledge (which is general) onto the given layout (which is specific). Extensive experiments show that the proposed diffusion network produces realistic and freestyle layout-to-image generation results with diverse text inputs, which has a high potential to spawn a bunch of interesting applications. Code is available at https://github.com/essunny310/FreestyleNet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xue_Freestyle_Layout-to-Image_Synthesis_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xue_Freestyle_Layout-to-Image_Synthesis_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14412 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xue_Freestyle_Layout-to-Image_Synthesis_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xue_Freestyle_Layout-to-Image_Synthesis_CVPR_2023_paper.html | CVPR 2023 | null |
Effective Ambiguity Attack Against Passport-Based DNN Intellectual Property Protection Schemes Through Fully Connected Layer Substitution | Yiming Chen, Jinyu Tian, Xiangyu Chen, Jiantao Zhou | Since training a deep neural network (DNN) is costly, the well-trained deep models can be regarded as valuable intellectual property (IP) assets. The IP protection associated with deep models has been receiving increasing attentions in recent years. Passport-based method, which replaces normalization layers with passport layers, has been one of the few protection solutions that are claimed to be secure against advanced attacks. In this work, we tackle the issue of evaluating the security of passport-based IP protection methods. We propose a novel and effective ambiguity attack against passport-based method, capable of successfully forging multiple valid passports with a small training dataset. This is accomplished by inserting a specially designed accessory block ahead of the passport parameters. Using less than 10% of training data, with the forged passport, the model exhibits almost indistinguishable performance difference (less than 2%) compared with that of the authorized passport. In addition, it is shown that our attack strategy can be readily generalized to attack other IP protection methods based on watermark embedding. Directions for potential remedy solutions are also given. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Effective_Ambiguity_Attack_Against_Passport-Based_DNN_Intellectual_Property_Protection_Schemes_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Effective_Ambiguity_Attack_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11595 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Effective_Ambiguity_Attack_Against_Passport-Based_DNN_Intellectual_Property_Protection_Schemes_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Effective_Ambiguity_Attack_Against_Passport-Based_DNN_Intellectual_Property_Protection_Schemes_CVPR_2023_paper.html | CVPR 2023 | null |
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