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MobileBrick: Building LEGO for 3D Reconstruction on Mobile Devices | null | null | null | null | null | null | https://openaccess.thecvf.com/content/CVPR2023/html/Li_MobileBrick_Building_LEGO_for_3D_Reconstruction_on_Mobile_Devices_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_MobileBrick_Building_LEGO_for_3D_Reconstruction_on_Mobile_Devices_CVPR_2023_paper.html | CVPR 2023 | null |
GKEAL: Gaussian Kernel Embedded Analytic Learning for Few-Shot Class Incremental Task | Huiping Zhuang, Zhenyu Weng, Run He, Zhiping Lin, Ziqian Zeng | Few-shot class incremental learning (FSCIL) aims to address catastrophic forgetting during class incremental learning in a few-shot learning setting. In this paper, we approach the FSCIL by adopting analytic learning, a technique that converts network training into linear problems. This is inspired by the fact that the recursive implementation (batch-by-batch learning) of analytic learning gives identical weights to that produced by training on the entire dataset at once. The recursive implementation and the weight-identical property highly resemble the FSCIL setting (phase-by-phase learning) and its goal of avoiding catastrophic forgetting. By bridging the FSCIL with the analytic learning, we propose a Gaussian kernel embedded analytic learning (GKEAL) for FSCIL. The key components of GKEAL include the kernel analytic module which allows the GKEAL to conduct FSCIL in a recursive manner, and the augmented feature concatenation module that balances the preference between old and new tasks especially effectively under the few-shot setting. Our experiments show that the GKEAL gives state-of-the-art performance on several benchmark datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhuang_GKEAL_Gaussian_Kernel_Embedded_Analytic_Learning_for_Few-Shot_Class_Incremental_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhuang_GKEAL_Gaussian_Kernel_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhuang_GKEAL_Gaussian_Kernel_Embedded_Analytic_Learning_for_Few-Shot_Class_Incremental_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhuang_GKEAL_Gaussian_Kernel_Embedded_Analytic_Learning_for_Few-Shot_Class_Incremental_CVPR_2023_paper.html | CVPR 2023 | null |
SteerNeRF: Accelerating NeRF Rendering via Smooth Viewpoint Trajectory | Sicheng Li, Hao Li, Yue Wang, Yiyi Liao, Lu Yu | Neural Radiance Fields (NeRF) have demonstrated superior novel view synthesis performance but are slow at rendering. To speed up the volume rendering process, many acceleration methods have been proposed at the cost of large memory consumption. To push the frontier of the efficiency-memory trade-off, we explore a new perspective to accelerate NeRF rendering, leveraging a key fact that the viewpoint change is usually smooth and continuous in interactive viewpoint control. This allows us to leverage the information of preceding viewpoints to reduce the number of rendered pixels as well as the number of sampled points along the ray of the remaining pixels. In our pipeline, a low-resolution feature map is rendered first by volume rendering, then a lightweight 2D neural renderer is applied to generate the output image at target resolution leveraging the features of preceding and current frames. We show that the proposed method can achieve competitive rendering quality while reducing the rendering time with little memory overhead, enabling 30FPS at 1080P image resolution with a low memory footprint. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_SteerNeRF_Accelerating_NeRF_Rendering_via_Smooth_Viewpoint_Trajectory_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2212.08476 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_SteerNeRF_Accelerating_NeRF_Rendering_via_Smooth_Viewpoint_Trajectory_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_SteerNeRF_Accelerating_NeRF_Rendering_via_Smooth_Viewpoint_Trajectory_CVPR_2023_paper.html | CVPR 2023 | null |
Active Exploration of Multimodal Complementarity for Few-Shot Action Recognition | Yuyang Wanyan, Xiaoshan Yang, Chaofan Chen, Changsheng Xu | Recently, few-shot action recognition receives increasing attention and achieves remarkable progress. However, previous methods mainly rely on limited unimodal data (e.g., RGB frames) while the multimodal information remains relatively underexplored. In this paper, we propose a novel Active Multimodal Few-shot Action Recognition (AMFAR) framework, which can actively find the reliable modality for each sample based on task-dependent context information to improve few-shot reasoning procedure. In meta-training, we design an Active Sample Selection (ASS) module to organize query samples with large differences in the reliability of modalities into different groups based on modality-specific posterior distributions. In addition, we design an Active Mutual Distillation (AMD) module to capture discriminative task-specific knowledge from the reliable modality to improve the representation learning of unreliable modality by bidirectional knowledge distillation. In meta-test, we adopt Adaptive Multimodal Inference (AMI) module to adaptively fuse the modality-specific posterior distributions with a larger weight on the reliable modality. Extensive experimental results on four public benchmarks demonstrate that our model achieves significant improvements over existing unimodal and multimodal methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wanyan_Active_Exploration_of_Multimodal_Complementarity_for_Few-Shot_Action_Recognition_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wanyan_Active_Exploration_of_Multimodal_Complementarity_for_Few-Shot_Action_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wanyan_Active_Exploration_of_Multimodal_Complementarity_for_Few-Shot_Action_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
Magic3D: High-Resolution Text-to-3D Content Creation | Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, Tsung-Yi Lin | Recently, DreamFusion demonstrated the utility of a pretrained text-to-image diffusion model to optimize Neural Radiance Fields (NeRF), achieving remarkable text-to-3D synthesis results. However, the method has two inherent limitations: 1) optimization of the NeRF representation is extremely slow, 2) NeRF is supervised by images at a low resolution (64x64), thus leading to low-quality 3D models with a long wait time. In this paper, we address these limitations by utilizing a two-stage coarse-to-fine optimization framework. In the first stage, we use a sparse 3D neural representation to accelerate optimization while using a low-resolution diffusion prior. In the second stage, we use a textured mesh model initialized from the coarse neural representation, allowing us to perform optimization with a very efficient differentiable renderer interacting with high-resolution images. Our method, dubbed Magic3D, can create a 3D mesh model in 40 minutes, 2x faster than DreamFusion (reportedly taking 1.5 hours on average), while achieving 8x higher resolution. User studies show 61.7% raters to prefer our approach than DreamFusion. Together with the image-conditioned generation capabilities, we provide users with new ways to control 3D synthesis, opening up new avenues to various creative applications. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lin_Magic3D_High-Resolution_Text-to-3D_Content_Creation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lin_Magic3D_High-Resolution_Text-to-3D_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.10440 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Magic3D_High-Resolution_Text-to-3D_Content_Creation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Magic3D_High-Resolution_Text-to-3D_Content_Creation_CVPR_2023_paper.html | CVPR 2023 | null |
Boundary-Aware Backward-Compatible Representation via Adversarial Learning in Image Retrieval | Tan Pan, Furong Xu, Xudong Yang, Sifeng He, Chen Jiang, Qingpei Guo, Feng Qian, Xiaobo Zhang, Yuan Cheng, Lei Yang, Wei Chu | Image retrieval plays an important role in the Internet world. Usually, the core parts of mainstream visual retrieval systems include an online service of the embedding model and a large-scale vector database. For traditional model upgrades, the old model will not be replaced by the new one until the embeddings of all the images in the database are re-computed by the new model, which takes days or weeks for a large amount of data. Recently, backward-compatible training (BCT) enables the new model to be immediately deployed online by making the new embeddings directly comparable to the old ones. For BCT, improving the compatibility of two models with less negative impact on retrieval performance is the key challenge. In this paper, we introduce AdvBCT, an Adversarial Backward-Compatible Training method with an elastic boundary constraint that takes both compatibility and discrimination into consideration. We first employ adversarial learning to minimize the distribution disparity between embeddings of the new model and the old model. Meanwhile, we add an elastic boundary constraint during training to improve compatibility and discrimination efficiently. Extensive experiments on GLDv2, Revisited Oxford (ROxford), and Revisited Paris (RParis) demonstrate that our method outperforms other BCT methods on both compatibility and discrimination. The implementation of AdvBCT will be publicly available at https://github.com/Ashespt/AdvBCT. | https://openaccess.thecvf.com/content/CVPR2023/papers/Pan_Boundary-Aware_Backward-Compatible_Representation_via_Adversarial_Learning_in_Image_Retrieval_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Pan_Boundary-Aware_Backward-Compatible_Representation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.02610 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Pan_Boundary-Aware_Backward-Compatible_Representation_via_Adversarial_Learning_in_Image_Retrieval_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Pan_Boundary-Aware_Backward-Compatible_Representation_via_Adversarial_Learning_in_Image_Retrieval_CVPR_2023_paper.html | CVPR 2023 | null |
Spatial-Frequency Mutual Learning for Face Super-Resolution | Chenyang Wang, Junjun Jiang, Zhiwei Zhong, Xianming Liu | Face super-resolution (FSR) aims to reconstruct high-resolution (HR) face images from the low-resolution (LR) ones. With the advent of deep learning, the FSR technique has achieved significant breakthroughs. However, existing FSR methods either have a fixed receptive field or fail to maintain facial structure, limiting the FSR performance. To circumvent this problem, Fourier transform is introduced, which can capture global facial structure information and achieve image-size receptive field. Relying on the Fourier transform, we devise a spatial-frequency mutual network (SFMNet) for FSR, which is the first FSR method to explore the correlations between spatial and frequency domains as far as we know. To be specific, our SFMNet is a two-branch network equipped with a spatial branch and a frequency branch. Benefiting from the property of Fourier transform, the frequency branch can achieve image-size receptive field and capture global dependency while the spatial branch can extract local dependency. Considering that these dependencies are complementary and both favorable for FSR, we further develop a frequency-spatial interaction block (FSIB) which mutually amalgamates the complementary spatial and frequency information to enhance the capability of the model. Quantitative and qualitative experimental results show that the proposed method outperforms state-of-the-art FSR methods in recovering face images. The implementation and model will be released at https://github.com/wcy-cs/SFMNet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Spatial-Frequency_Mutual_Learning_for_Face_Super-Resolution_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Spatial-Frequency_Mutual_Learning_for_Face_Super-Resolution_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Spatial-Frequency_Mutual_Learning_for_Face_Super-Resolution_CVPR_2023_paper.html | CVPR 2023 | null |
Sketch2Saliency: Learning To Detect Salient Objects From Human Drawings | Ayan Kumar Bhunia, Subhadeep Koley, Amandeep Kumar, Aneeshan Sain, Pinaki Nath Chowdhury, Tao Xiang, Yi-Zhe Song | Human sketch has already proved its worth in various visual understanding tasks (e.g., retrieval, segmentation, image-captioning, etc). In this paper, we reveal a new trait of sketches -- that they are also salient. This is intuitive as sketching is a natural attentive process at its core. More specifically, we aim to study how sketches can be used as a weak label to detect salient objects present in an image. To this end, we propose a novel method that emphasises on how "salient object" could be explained by hand-drawn sketches. To accomplish this, we introduce a photo-to-sketch generation model that aims to generate sequential sketch coordinates corresponding to a given visual photo through a 2D attention mechanism. Attention maps accumulated across the time steps give rise to salient regions in the process. Extensive quantitative and qualitative experiments prove our hypothesis and delineate how our sketch-based saliency detection model gives a competitive performance compared to the state-of-the-art. | https://openaccess.thecvf.com/content/CVPR2023/papers/Bhunia_Sketch2Saliency_Learning_To_Detect_Salient_Objects_From_Human_Drawings_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bhunia_Sketch2Saliency_Learning_To_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11502 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bhunia_Sketch2Saliency_Learning_To_Detect_Salient_Objects_From_Human_Drawings_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bhunia_Sketch2Saliency_Learning_To_Detect_Salient_Objects_From_Human_Drawings_CVPR_2023_paper.html | CVPR 2023 | null |
Efficient Frequency Domain-Based Transformers for High-Quality Image Deblurring | Lingshun Kong, Jiangxin Dong, Jianjun Ge, Mingqiang Li, Jinshan Pan | We present an effective and efficient method that explores the properties of Transformers in the frequency domain for high-quality image deblurring. Our method is motivated by the convolution theorem that the correlation or convolution of two signals in the spatial domain is equivalent to an element-wise product of them in the frequency domain. This inspires us to develop an efficient frequency domain-based self-attention solver (FSAS) to estimate the scaled dot-product attention by an element-wise product operation instead of the matrix multiplication in the spatial domain. In addition, we note that simply using the naive feed-forward network (FFN) in Transformers does not generate good deblurred results. To overcome this problem, we propose a simple yet effective discriminative frequency domain-based FFN (DFFN), where we introduce a gated mechanism in the FFN based on the Joint Photographic Experts Group (JPEG) compression algorithm to discriminatively determine which low- and high-frequency information of the features should be preserved for latent clear image restoration. We formulate the proposed FSAS and DFFN into an asymmetrical network based on an encoder and decoder architecture, where the FSAS is only used in the decoder module for better image deblurring. Experimental results show that the proposed method performs favorably against the state-of-the-art approaches. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kong_Efficient_Frequency_Domain-Based_Transformers_for_High-Quality_Image_Deblurring_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kong_Efficient_Frequency_Domain-Based_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.12250 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kong_Efficient_Frequency_Domain-Based_Transformers_for_High-Quality_Image_Deblurring_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kong_Efficient_Frequency_Domain-Based_Transformers_for_High-Quality_Image_Deblurring_CVPR_2023_paper.html | CVPR 2023 | null |
Distilling Focal Knowledge From Imperfect Expert for 3D Object Detection | Jia Zeng, Li Chen, Hanming Deng, Lewei Lu, Junchi Yan, Yu Qiao, Hongyang Li | Multi-camera 3D object detection blossoms in recent years and most of state-of-the-art methods are built up on the bird's-eye-view (BEV) representations. Albeit remarkable performance, these works suffer from low efficiency. Typically, knowledge distillation can be used for model compression. However, due to unclear 3D geometry reasoning, expert features usually contain some noisy and confusing areas. In this work, we investigate on how to distill the knowledge from an imperfect expert. We propose FD3D, a Focal Distiller for 3D object detection. Specifically, a set of queries are leveraged to locate the instance-level areas for masked feature generation, to intensify feature representation ability in these areas. Moreover, these queries search out the representative fine-grained positions for refined distillation. We verify the effectiveness of our method by applying it to two popular detection models, BEVFormer and DETR3D. The results demonstrate that our method achieves improvements of 4.07 and 3.17 points respectively in terms of NDS metric on nuScenes benchmark. Code is hosted at https://github.com/OpenPerceptionX/BEVPerception-Survey-Recipe. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zeng_Distilling_Focal_Knowledge_From_Imperfect_Expert_for_3D_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zeng_Distilling_Focal_Knowledge_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zeng_Distilling_Focal_Knowledge_From_Imperfect_Expert_for_3D_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zeng_Distilling_Focal_Knowledge_From_Imperfect_Expert_for_3D_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding | Le Xue, Mingfei Gao, Chen Xing, Roberto Martín-Martín, Jiajun Wu, Caiming Xiong, Ran Xu, Juan Carlos Niebles, Silvio Savarese | The recognition capabilities of current state-of-the-art 3D models are limited by datasets with a small number of annotated data and a pre-defined set of categories. In its 2D counterpart, recent advances have shown that similar problems can be significantly alleviated by employing knowledge from other modalities, such as language. Inspired by this, leveraging multimodal information for 3D modality could be promising to improve 3D understanding under the restricted data regime, but this line of research is not well studied. Therefore, we introduce ULIP to learn a unified representation of images, language, and 3D point clouds by pre-training with object triplets from the three modalities. To overcome the shortage of training triplets, ULIP leverages a pre-trained vision-language model that has already learned a common visual and textual space by training with massive image-text pairs. Then, ULIP learns a 3D representation space aligned with the common image-text space, using a small number of automatically synthesized triplets. ULIP is agnostic to 3D backbone networks and can easily be integrated into any 3D architecture. Experiments show that ULIP effectively improves the performance of multiple recent 3D backbones by simply pre-training them on ShapeNet55 using our framework, achieving state-of-the-art performance in both standard 3D classification and zero-shot 3D classification on ModelNet40 and ScanObjectNN. ULIP also improves the performance of PointMLP by around 3% in 3D classification on ScanObjectNN, and outperforms PointCLIP by 28.8% on top-1 accuracy for zero-shot 3D classification on ModelNet40. Our code and pre-trained models are released at https://github.com/salesforce/ULIP. | https://openaccess.thecvf.com/content/CVPR2023/papers/Gao_ULIP_Learning_a_Unified_Representation_of_Language_Images_and_Point_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gao_ULIP_Learning_a_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_ULIP_Learning_a_Unified_Representation_of_Language_Images_and_Point_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_ULIP_Learning_a_Unified_Representation_of_Language_Images_and_Point_CVPR_2023_paper.html | CVPR 2023 | null |
Being Comes From Not-Being: Open-Vocabulary Text-to-Motion Generation With Wordless Training | Junfan Lin, Jianlong Chang, Lingbo Liu, Guanbin Li, Liang Lin, Qi Tian, Chang-Wen Chen | Text-to-motion generation is an emerging and challenging problem, which aims to synthesize motion with the same semantics as the input text. However, due to the lack of diverse labeled training data, most approaches either limit to specific types of text annotations or require online optimizations to cater to the texts during inference at the cost of efficiency and stability. In this paper, we investigate offline open-vocabulary text-to-motion generation in a zero-shot learning manner that neither requires paired training data nor extra online optimization to adapt for unseen texts. Inspired by the prompt learning in NLP, we pretrain a motion generator that learns to reconstruct the full motion from the masked motion. During inference, instead of changing the motion generator, our method reformulates the input text into a masked motion as the prompt for the motion generator to "reconstruct" the motion. In constructing the prompt, the unmasked poses of the prompt are synthesized by a text-to-pose generator. To supervise the optimization of the text-to-pose generator, we propose the first text-pose alignment model for measuring the alignment between texts and 3D poses. And to prevent the pose generator from overfitting to limited training texts, we further propose a novel wordless training mechanism that optimizes the text-to-pose generator without any training texts. The comprehensive experimental results show that our method obtains a significant improvement against the baseline methods. The code is available at https://github.com/junfanlin/oohmg. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lin_Being_Comes_From_Not-Being_Open-Vocabulary_Text-to-Motion_Generation_With_Wordless_Training_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lin_Being_Comes_From_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Being_Comes_From_Not-Being_Open-Vocabulary_Text-to-Motion_Generation_With_Wordless_Training_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Being_Comes_From_Not-Being_Open-Vocabulary_Text-to-Motion_Generation_With_Wordless_Training_CVPR_2023_paper.html | CVPR 2023 | null |
Deep Learning of Partial Graph Matching via Differentiable Top-K | Runzhong Wang, Ziao Guo, Shaofei Jiang, Xiaokang Yang, Junchi Yan | Graph matching (GM) aims at discovering node matching between graphs, by maximizing the node- and edge-wise affinities between the matched elements. As an NP-hard problem, its challenge is further pronounced in the existence of outlier nodes in both graphs which is ubiquitous in practice, especially for vision problems. However, popular affinity-maximization-based paradigms often lack a principled scheme to suppress the false matching and resort to handcrafted thresholding to dismiss the outliers. This limitation is also inherited by the neural GM solvers though they have shown superior performance in the ideal no-outlier setting. In this paper, we propose to formulate the partial GM problem as the top-k selection task with a given/estimated number of inliers k. Specifically, we devise a differentiable top-k module that enables effective gradient descent over the optimal-transport layer, which can be readily plugged into SOTA deep GM pipelines including the quadratic matching network NGMv2 as well as the linear matching network GCAN. Meanwhile, the attention-fused aggregation layers are developed to estimate k to enable automatic outlier-robust matching in the wild. Last but not least, we remake and release a new benchmark called IMC-PT-SparseGM, originating from the IMC-PT stereo-matching dataset. The new benchmark involves more scale-varying graphs and partial matching instances from the real world. Experiments show that our methods outperform other partial matching schemes on popular benchmarks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Deep_Learning_of_Partial_Graph_Matching_via_Differentiable_Top-K_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Deep_Learning_of_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Deep_Learning_of_Partial_Graph_Matching_via_Differentiable_Top-K_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Deep_Learning_of_Partial_Graph_Matching_via_Differentiable_Top-K_CVPR_2023_paper.html | CVPR 2023 | null |
Super-CLEVR: A Virtual Benchmark To Diagnose Domain Robustness in Visual Reasoning | Zhuowan Li, Xingrui Wang, Elias Stengel-Eskin, Adam Kortylewski, Wufei Ma, Benjamin Van Durme, Alan L. Yuille | Visual Question Answering (VQA) models often perform poorly on out-of-distribution data and struggle on domain generalization. Due to the multi-modal nature of this task, multiple factors of variation are intertwined, making generalization difficult to analyze. This motivates us to introduce a virtual benchmark, Super-CLEVR, where different factors in VQA domain shifts can be isolated in order that their effects can be studied independently. Four factors are considered: visual complexity, question redundancy, concept distribution and concept compositionality. With controllably generated data, Super-CLEVR enables us to test VQA methods in situations where the test data differs from the training data along each of these axes. We study four existing methods, including two neural symbolic methods NSCL and NSVQA, and two non-symbolic methods FiLM and mDETR; and our proposed method, probabilistic NSVQA (P-NSVQA), which extends NSVQA with uncertainty reasoning. P-NSVQA outperforms other methods on three of the four domain shift factors. Our results suggest that disentangling reasoning and perception, combined with probabilistic uncertainty, form a strong VQA model that is more robust to domain shifts. The dataset and code are released at https://github.com/Lizw14/Super-CLEVR. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Super-CLEVR_A_Virtual_Benchmark_To_Diagnose_Domain_Robustness_in_Visual_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Super-CLEVR_A_Virtual_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Super-CLEVR_A_Virtual_Benchmark_To_Diagnose_Domain_Robustness_in_Visual_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Super-CLEVR_A_Virtual_Benchmark_To_Diagnose_Domain_Robustness_in_Visual_CVPR_2023_paper.html | CVPR 2023 | null |
MonoHuman: Animatable Human Neural Field From Monocular Video | Zhengming Yu, Wei Cheng, Xian Liu, Wayne Wu, Kwan-Yee Lin | Animating virtual avatars with free-view control is crucial for various applications like virtual reality and digital entertainment. Previous studies have attempted to utilize the representation power of the neural radiance field (NeRF) to reconstruct the human body from monocular videos. Recent works propose to graft a deformation network into the NeRF to further model the dynamics of the human neural field for animating vivid human motions. However, such pipelines either rely on pose-dependent representations or fall short of motion coherency due to frame-independent optimization, making it difficult to generalize to unseen pose sequences realistically. In this paper, we propose a novel framework MonoHuman, which robustly renders view-consistent and high-fidelity avatars under arbitrary novel poses. Our key insight is to model the deformation field with bi-directional constraints and explicitly leverage the off-the-peg keyframe information to reason the feature correlations for coherent results. Specifically, we first propose a Shared Bidirectional Deformation module, which creates a pose-independent generalizable deformation field by disentangling backward and forward deformation correspondences into shared skeletal motion weight and separate non-rigid motions. Then, we devise a Forward Correspondence Search module, which queries the correspondence feature of keyframes to guide the rendering network. The rendered results are thus multi-view consistent with high fidelity, even under challenging novel pose settings. Extensive experiments demonstrate the superiority of our proposed MonoHuman over state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_MonoHuman_Animatable_Human_Neural_Field_From_Monocular_Video_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_MonoHuman_Animatable_Human_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.02001 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_MonoHuman_Animatable_Human_Neural_Field_From_Monocular_Video_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_MonoHuman_Animatable_Human_Neural_Field_From_Monocular_Video_CVPR_2023_paper.html | CVPR 2023 | null |
Sliced Optimal Partial Transport | Yikun Bai, Bernhard Schmitzer, Matthew Thorpe, Soheil Kolouri | Optimal transport (OT) has become exceedingly popular in machine learning, data science, and computer vision. The core assumption in the OT problem is the equal total amount of mass in source and target measures, which limits its application. Optimal Partial Transport (OPT) is a recently proposed solution to this limitation. Similar to the OT problem, the computation of OPT relies on solving a linear programming problem (often in high dimensions), which can become computationally prohibitive. In this paper, we propose an efficient algorithm for calculating the OPT problem between two non-negative measures in one dimension. Next, following the idea of sliced OT distances, we utilize slicing to define the sliced OPT distance. Finally, we demonstrate the computational and accuracy benefits of the sliced OPT-based method in various numerical experiments. In particular, we show an application of our proposed Sliced-OPT in noisy point cloud registration. | https://openaccess.thecvf.com/content/CVPR2023/papers/Bai_Sliced_Optimal_Partial_Transport_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bai_Sliced_Optimal_Partial_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.08049 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bai_Sliced_Optimal_Partial_Transport_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bai_Sliced_Optimal_Partial_Transport_CVPR_2023_paper.html | CVPR 2023 | null |
Siamese DETR | Zeren Chen, Gengshi Huang, Wei Li, Jianing Teng, Kun Wang, Jing Shao, Chen Change Loy, Lu Sheng | Recent self-supervised methods are mainly designed for representation learning with the base model, e.g., ResNets or ViTs. They cannot be easily transferred to DETR, with task-specific Transformer modules. In this work, we present Siamese DETR, a Siamese self-supervised pretraining approach for the Transformer architecture in DETR. We consider learning view-invariant and detection-oriented representations simultaneously through two complementary tasks, i.e., localization and discrimination, in a novel multi-view learning framework. Two self-supervised pretext tasks are designed: (i) Multi-View Region Detection aims at learning to localize regions-of-interest between augmented views of the input, and (ii) Multi-View Semantic Discrimination attempts to improve object-level discrimination for each region. The proposed Siamese DETR achieves state-of-the-art transfer performance on COCO and PASCAL VOC detection using different DETR variants in all setups. Code is available at https://github.com/Zx55/SiameseDETR. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Siamese_DETR_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_Siamese_DETR_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.18144 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Siamese_DETR_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Siamese_DETR_CVPR_2023_paper.html | CVPR 2023 | null |
SINE: Semantic-Driven Image-Based NeRF Editing With Prior-Guided Editing Field | Chong Bao, Yinda Zhang, Bangbang Yang, Tianxing Fan, Zesong Yang, Hujun Bao, Guofeng Zhang, Zhaopeng Cui | Despite the great success in 2D editing using user-friendly tools, such as Photoshop, semantic strokes, or even text prompts, similar capabilities in 3D areas are still limited, either relying on 3D modeling skills or allowing editing within only a few categories. In this paper, we present a novel semantic-driven NeRF editing approach, which enables users to edit a neural radiance field with a single image, and faithfully delivers edited novel views with high fidelity and multi-view consistency. To achieve this goal, we propose a prior-guided editing field to encode fine-grained geometric and texture editing in 3D space, and develop a series of techniques to aid the editing process, including cyclic constraints with a proxy mesh to facilitate geometric supervision, a color compositing mechanism to stabilize semantic-driven texture editing, and a feature-cluster-based regularization to preserve the irrelevant content unchanged. Extensive experiments and editing examples on both real-world and synthetic data demonstrate that our method achieves photo-realistic 3D editing using only a single edited image, pushing the bound of semantic-driven editing in 3D real-world scenes. | https://openaccess.thecvf.com/content/CVPR2023/papers/Bao_SINE_Semantic-Driven_Image-Based_NeRF_Editing_With_Prior-Guided_Editing_Field_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bao_SINE_Semantic-Driven_Image-Based_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.13277 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bao_SINE_Semantic-Driven_Image-Based_NeRF_Editing_With_Prior-Guided_Editing_Field_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bao_SINE_Semantic-Driven_Image-Based_NeRF_Editing_With_Prior-Guided_Editing_Field_CVPR_2023_paper.html | CVPR 2023 | null |
Turning Strengths Into Weaknesses: A Certified Robustness Inspired Attack Framework Against Graph Neural Networks | Binghui Wang, Meng Pang, Yun Dong | Graph neural networks (GNNs) have achieved state-of-the-art performance in many graph-related tasks such as node classification. However, recent studies show that GNNs are vulnerable to both test-time and training-time attacks that perturb the graph structure. While the existing attack methods have shown promising attack performance, we would like to design an attack framework that can significantly enhance both the existing evasion and poisoning attacks. In particular, our attack framework is inspired by certified robustness. Certified robustness was originally used by defenders to defend against adversarial attacks. We are the first, from the attacker perspective, to leverage its properties to better attack GNNs. Specifically, we first leverage and derive nodes' certified perturbation sizes against evasion and poisoning attacks based on randomized smoothing. A larger certified perturbation size of a node indicates this node is theoretically more robust to graph perturbations. Such a property motivates us to focus more on nodes with smaller certified perturbation sizes, as they are easier to be attacked after graph perturbations. Accordingly, we design a certified robustness inspired attack loss, when incorporated into (any) existing attacks, produces our certified robustness inspired attack framework. We apply our attack framework to the existing attacks and results show it can significantly enhance the existing attacks' performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Turning_Strengths_Into_Weaknesses_A_Certified_Robustness_Inspired_Attack_Framework_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Turning_Strengths_Into_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.06199 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Turning_Strengths_Into_Weaknesses_A_Certified_Robustness_Inspired_Attack_Framework_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Turning_Strengths_Into_Weaknesses_A_Certified_Robustness_Inspired_Attack_Framework_CVPR_2023_paper.html | CVPR 2023 | null |
Demystifying Causal Features on Adversarial Examples and Causal Inoculation for Robust Network by Adversarial Instrumental Variable Regression | Junho Kim, Byung-Kwan Lee, Yong Man Ro | The origin of adversarial examples is still inexplicable in research fields, and it arouses arguments from various viewpoints, albeit comprehensive investigations. In this paper, we propose a way of delving into the unexpected vulnerability in adversarially trained networks from a causal perspective, namely adversarial instrumental variable (IV) regression. By deploying it, we estimate the causal relation of adversarial prediction under an unbiased environment dissociated from unknown confounders. Our approach aims to demystify inherent causal features on adversarial examples by leveraging a zero-sum optimization game between a casual feature estimator (i.e., hypothesis model) and worst-case counterfactuals (i.e., test function) disturbing to find causal features. Through extensive analyses, we demonstrate that the estimated causal features are highly related to the correct prediction for adversarial robustness, and the counterfactuals exhibit extreme features significantly deviating from the correct prediction. In addition, we present how to effectively inoculate CAusal FEatures (CAFE) into defense networks for improving adversarial robustness. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kim_Demystifying_Causal_Features_on_Adversarial_Examples_and_Causal_Inoculation_for_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kim_Demystifying_Causal_Features_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.01052 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Demystifying_Causal_Features_on_Adversarial_Examples_and_Causal_Inoculation_for_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Demystifying_Causal_Features_on_Adversarial_Examples_and_Causal_Inoculation_for_CVPR_2023_paper.html | CVPR 2023 | null |
NVTC: Nonlinear Vector Transform Coding | Runsen Feng, Zongyu Guo, Weiping Li, Zhibo Chen | In theory, vector quantization (VQ) is always better than scalar quantization (SQ) in terms of rate-distortion (R-D) performance. Recent state-of-the-art methods for neural image compression are mainly based on nonlinear transform coding (NTC) with uniform scalar quantization, overlooking the benefits of VQ due to its exponentially increased complexity. In this paper, we first investigate on some toy sources, demonstrating that even if modern neural networks considerably enhance the compression performance of SQ with nonlinear transform, there is still an insurmountable chasm between SQ and VQ. Therefore, revolving around VQ, we propose a novel framework for neural image compression named Nonlinear Vector Transform Coding (NVTC). NVTC solves the critical complexity issue of VQ through (1) a multi-stage quantization strategy and (2) nonlinear vector transforms. In addition, we apply entropy-constrained VQ in latent space to adaptively determine the quantization boundaries for joint rate-distortion optimization, which improves the performance both theoretically and experimentally. Compared to previous NTC approaches, NVTC demonstrates superior rate-distortion performance, faster decoding speed, and smaller model size. Our code is available at https://github.com/USTC-IMCL/NVTC. | https://openaccess.thecvf.com/content/CVPR2023/papers/Feng_NVTC_Nonlinear_Vector_Transform_Coding_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Feng_NVTC_Nonlinear_Vector_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_NVTC_Nonlinear_Vector_Transform_Coding_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_NVTC_Nonlinear_Vector_Transform_Coding_CVPR_2023_paper.html | CVPR 2023 | null |
B-Spline Texture Coefficients Estimator for Screen Content Image Super-Resolution | Byeonghyun Pak, Jaewon Lee, Kyong Hwan Jin | Screen content images (SCIs) include many informative components, e.g., texts and graphics. Such content creates sharp edges or homogeneous areas, making a pixel distribution of SCI different from the natural image. Therefore, we need to properly handle the edges and textures to minimize information distortion of the contents when a display device's resolution differs from SCIs. To achieve this goal, we propose an implicit neural representation using B-splines for screen content image super-resolution (SCI SR) with arbitrary scales. Our method extracts scaling, translating, and smoothing parameters of B-splines. The followed multi-layer perceptron (MLP) uses the estimated B-splines to recover high-resolution SCI. Our network outperforms both a transformer-based reconstruction and an implicit Fourier representation method in almost upscaling factor, thanks to the positive constraint and compact support of the B-spline basis. Moreover, our SR results are recognized as correct text letters with the highest confidence by a pre-trained scene text recognition network. Source code is available at https://github.com/ByeongHyunPak/btc. | https://openaccess.thecvf.com/content/CVPR2023/papers/Pak_B-Spline_Texture_Coefficients_Estimator_for_Screen_Content_Image_Super-Resolution_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Pak_B-Spline_Texture_Coefficients_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Pak_B-Spline_Texture_Coefficients_Estimator_for_Screen_Content_Image_Super-Resolution_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Pak_B-Spline_Texture_Coefficients_Estimator_for_Screen_Content_Image_Super-Resolution_CVPR_2023_paper.html | CVPR 2023 | null |
MetaCLUE: Towards Comprehensive Visual Metaphors Research | Arjun R. Akula, Brendan Driscoll, Pradyumna Narayana, Soravit Changpinyo, Zhiwei Jia, Suyash Damle, Garima Pruthi, Sugato Basu, Leonidas Guibas, William T. Freeman, Yuanzhen Li, Varun Jampani | Creativity is an indispensable part of human cognition and also an inherent part of how we make sense of the world. Metaphorical abstraction is fundamental in communicating creative ideas through nuanced relationships between abstract concepts such as feelings. While computer vision benchmarks and approaches predominantly focus on understanding and generating literal interpretations of images, metaphorical comprehension of images remains relatively unexplored. Towards this goal, we introduce MetaCLUE, a set of vision tasks on visual metaphor. We also collect high-quality and rich metaphor annotations (abstract objects, concepts, relationships along with their corresponding object boxes) as there do not exist any datasets that facilitate the evaluation of these tasks. We perform a comprehensive analysis of state-of-the-art models in vision and language based on our annotations, highlighting strengths and weaknesses of current approaches in visual metaphor Classification, Localization, Understanding (retrieval, question answering, captioning) and gEneration (text-to-image synthesis) tasks. We hope this work provides a concrete step towards systematically developing AI systems with human-like creative capabilities. Project page: https://metaclue.github.io | https://openaccess.thecvf.com/content/CVPR2023/papers/Akula_MetaCLUE_Towards_Comprehensive_Visual_Metaphors_Research_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Akula_MetaCLUE_Towards_Comprehensive_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.09898 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Akula_MetaCLUE_Towards_Comprehensive_Visual_Metaphors_Research_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Akula_MetaCLUE_Towards_Comprehensive_Visual_Metaphors_Research_CVPR_2023_paper.html | CVPR 2023 | null |
Towards End-to-End Generative Modeling of Long Videos With Memory-Efficient Bidirectional Transformers | Jaehoon Yoo, Semin Kim, Doyup Lee, Chiheon Kim, Seunghoon Hong | Autoregressive transformers have shown remarkable success in video generation. However, the transformers are prohibited from directly learning the long-term dependency in videos due to the quadratic complexity of self-attention, and inherently suffering from slow inference time and error propagation due to the autoregressive process. In this paper, we propose Memory-efficient Bidirectional Transformer (MeBT) for end-to-end learning of long-term dependency in videos and fast inference. Based on recent advances in bidirectional transformers, our method learns to decode the entire spatio-temporal volume of a video in parallel from partially observed patches. The proposed transformer achieves a linear time complexity in both encoding and decoding, by projecting observable context tokens into a fixed number of latent tokens and conditioning them to decode the masked tokens through the cross-attention. Empowered by linear complexity and bidirectional modeling, our method demonstrates significant improvement over the autoregressive Transformers for generating moderately long videos in both quality and speed. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yoo_Towards_End-to-End_Generative_Modeling_of_Long_Videos_With_Memory-Efficient_Bidirectional_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yoo_Towards_End-to-End_Generative_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11251 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yoo_Towards_End-to-End_Generative_Modeling_of_Long_Videos_With_Memory-Efficient_Bidirectional_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yoo_Towards_End-to-End_Generative_Modeling_of_Long_Videos_With_Memory-Efficient_Bidirectional_CVPR_2023_paper.html | CVPR 2023 | null |
Domain Expansion of Image Generators | Yotam Nitzan, Michaël Gharbi, Richard Zhang, Taesung Park, Jun-Yan Zhu, Daniel Cohen-Or, Eli Shechtman | Can one inject new concepts into an already trained generative model, while respecting its existing structure and knowledge? We propose a new task -- domain expansion -- to address this. Given a pretrained generator and novel (but related) domains, we expand the generator to jointly model all domains, old and new, harmoniously. First, we note the generator contains a meaningful, pretrained latent space. Is it possible to minimally perturb this hard-earned representation, while maximally representing the new domains? Interestingly, we find that the latent space offers unused, "dormant" axes, which do not affect the output. This provides an opportunity -- by "repurposing" these axes, we are able to represent new domains, without perturbing the original representation. In fact, we find that pretrained generators have the capacity to add several -- even hundreds -- of new domains! Using our expansion technique, one "expanded" model can supersede numerous domain-specific models, without expanding model size. Additionally, using a single, expanded generator natively supports smooth transitions between and composition of domains. | https://openaccess.thecvf.com/content/CVPR2023/papers/Nitzan_Domain_Expansion_of_Image_Generators_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Nitzan_Domain_Expansion_of_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.05225 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Nitzan_Domain_Expansion_of_Image_Generators_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Nitzan_Domain_Expansion_of_Image_Generators_CVPR_2023_paper.html | CVPR 2023 | null |
On the Effectiveness of Partial Variance Reduction in Federated Learning With Heterogeneous Data | null | null | null | null | null | null | https://openaccess.thecvf.com/content/CVPR2023/html/Li_On_the_Effectiveness_of_Partial_Variance_Reduction_in_Federated_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_On_the_Effectiveness_of_Partial_Variance_Reduction_in_Federated_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Point Cloud Forecasting as a Proxy for 4D Occupancy Forecasting | Tarasha Khurana, Peiyun Hu, David Held, Deva Ramanan | Predicting how the world can evolve in the future is crucial for motion planning in autonomous systems. Classical methods are limited because they rely on costly human annotations in the form of semantic class labels, bounding boxes, and tracks or HD maps of cities to plan their motion -- and thus are difficult to scale to large unlabeled datasets. One promising self-supervised task is 3D point cloud forecasting from unannotated LiDAR sequences. We show that this task requires algorithms to implicitly capture (1) sensor extrinsics (i.e., the egomotion of the autonomous vehicle), (2) sensor intrinsics (i.e., the sampling pattern specific to the particular LiDAR sensor), and (3) the shape and motion of other objects in the scene. But autonomous systems should make predictions about the world and not their sensors! To this end, we factor out (1) and (2) by recasting the task as one of spacetime (4D) occupancy forecasting. But because it is expensive to obtain ground-truth 4D occupancy, we "render" point cloud data from 4D occupancy predictions given sensor extrinsics and intrinsics, allowing one to train and test occupancy algorithms with unannotated LiDAR sequences. This also allows one to evaluate and compare point cloud forecasting algorithms across diverse datasets, sensors, and vehicles. | https://openaccess.thecvf.com/content/CVPR2023/papers/Khurana_Point_Cloud_Forecasting_as_a_Proxy_for_4D_Occupancy_Forecasting_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Khurana_Point_Cloud_Forecasting_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.13130 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Khurana_Point_Cloud_Forecasting_as_a_Proxy_for_4D_Occupancy_Forecasting_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Khurana_Point_Cloud_Forecasting_as_a_Proxy_for_4D_Occupancy_Forecasting_CVPR_2023_paper.html | CVPR 2023 | null |
Masked Representation Learning for Domain Generalized Stereo Matching | Zhibo Rao, Bangshu Xiong, Mingyi He, Yuchao Dai, Renjie He, Zhelun Shen, Xing Li | Recently, many deep stereo matching methods have begun to focus on cross-domain performance, achieving impressive achievements. However, these methods did not deal with the significant volatility of generalization performance among different training epochs. Inspired by masked representation learning and multi-task learning, this paper designs a simple and effective masked representation for domain generalized stereo matching. First, we feed the masked left and complete right images as input into the models. Then, we add a lightweight and simple decoder following the feature extraction module to recover the original left image. Finally, we train the models with two tasks (stereo matching and image reconstruction) as a pseudo-multi-task learning framework, promoting models to learn structure information and to improve generalization performance. We implement our method on two well-known architectures (CFNet and LacGwcNet) to demonstrate its effectiveness. Experimental results on multi-datasets show that: (1) our method can be easily plugged into the current various stereo matching models to improve generalization performance; (2) our method can reduce the significant volatility of generalization performance among different training epochs; (3) we find that the current methods prefer to choose the best results among different training epochs as generalization performance, but it is impossible to select the best performance by ground truth in practice. | https://openaccess.thecvf.com/content/CVPR2023/papers/Rao_Masked_Representation_Learning_for_Domain_Generalized_Stereo_Matching_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Rao_Masked_Representation_Learning_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Rao_Masked_Representation_Learning_for_Domain_Generalized_Stereo_Matching_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Rao_Masked_Representation_Learning_for_Domain_Generalized_Stereo_Matching_CVPR_2023_paper.html | CVPR 2023 | null |
LVQAC: Lattice Vector Quantization Coupled With Spatially Adaptive Companding for Efficient Learned Image Compression | Xi Zhang, Xiaolin Wu | Recently, numerous end-to-end optimized image compression neural networks have been developed and proved themselves as leaders in rate-distortion performance. The main strength of these learnt compression methods is in powerful nonlinear analysis and synthesis transforms that can be facilitated by deep neural networks. However, out of operational expediency, most of these end-to-end methods adopt uniform scalar quantizers rather than vector quantizers, which are information-theoretically optimal. In this paper, we present a novel Lattice Vector Quantization scheme coupled with a spatially Adaptive Companding (LVQAC) mapping. LVQ can better exploit the inter-feature dependencies than scalar uniform quantization while being computationally almost as simple as the latter. Moreover, to improve the adaptability of LVQ to source statistics, we couple a spatially adaptive companding (AC) mapping with LVQ. The resulting LVQAC design can be easily embedded into any end-to-end optimized image compression system. Extensive experiments demonstrate that for any end-to-end CNN image compression models, replacing uniform quantizer by LVQAC achieves better rate-distortion performance without significantly increasing the model complexity. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_LVQAC_Lattice_Vector_Quantization_Coupled_With_Spatially_Adaptive_Companding_for_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2304.12319 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_LVQAC_Lattice_Vector_Quantization_Coupled_With_Spatially_Adaptive_Companding_for_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_LVQAC_Lattice_Vector_Quantization_Coupled_With_Spatially_Adaptive_Companding_for_CVPR_2023_paper.html | CVPR 2023 | null |
You Can Ground Earlier Than See: An Effective and Efficient Pipeline for Temporal Sentence Grounding in Compressed Videos | Xiang Fang, Daizong Liu, Pan Zhou, Guoshun Nan | Given an untrimmed video, temporal sentence grounding (TSG) aims to locate a target moment semantically according to a sentence query. Although previous respectable works have made decent success, they only focus on high-level visual features extracted from the consecutive decoded frames and fail to handle the compressed videos for query modelling, suffering from insufficient representation capability and significant computational complexity during training and testing. In this paper, we pose a new setting, compressed-domain TSG, which directly utilizes compressed videos rather than fully-decompressed frames as the visual input. To handle the raw video bit-stream input, we propose a novel Three-branch Compressed-domain Spatial-temporal Fusion (TCSF) framework, which extracts and aggregates three kinds of low-level visual features (I-frame, motion vector and residual features) for effective and efficient grounding. Particularly, instead of encoding the whole decoded frames like previous works, we capture the appearance representation by only learning the I-frame feature to reduce delay or latency. Besides, we explore the motion information not only by learning the motion vector feature, but also by exploring the relations of neighboring frames via the residual feature. In this way, a three-branch spatial-temporal attention layer with an adaptive motion-appearance fusion module is further designed to extract and aggregate both appearance and motion information for the final grounding. Experiments on three challenging datasets shows that our TCSF achieves better performance than other state-of-the-art methods with lower complexity. | https://openaccess.thecvf.com/content/CVPR2023/papers/Fang_You_Can_Ground_Earlier_Than_See_An_Effective_and_Efficient_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.07863 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fang_You_Can_Ground_Earlier_Than_See_An_Effective_and_Efficient_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fang_You_Can_Ground_Earlier_Than_See_An_Effective_and_Efficient_CVPR_2023_paper.html | CVPR 2023 | null |
EqMotion: Equivariant Multi-Agent Motion Prediction With Invariant Interaction Reasoning | Chenxin Xu, Robby T. Tan, Yuhong Tan, Siheng Chen, Yu Guang Wang, Xinchao Wang, Yanfeng Wang | Learning to predict agent motions with relationship reasoning is important for many applications. In motion prediction tasks, maintaining motion equivariance under Euclidean geometric transformations and invariance of agent interaction is a critical and fundamental principle. However, such equivariance and invariance properties are overlooked by most existing methods. To fill this gap, we propose EqMotion, an efficient equivariant motion prediction model with invariant interaction reasoning. To achieve motion equivariance, we propose an equivariant geometric feature learning module to learn a Euclidean transformable feature through dedicated designs of equivariant operations. To reason agent's interactions, we propose an invariant interaction reasoning module to achieve a more stable interaction modeling. To further promote more comprehensive motion features, we propose an invariant pattern feature learning module to learn an invariant pattern feature, which cooperates with the equivariant geometric feature to enhance network expressiveness. We conduct experiments for the proposed model on four distinct scenarios: particle dynamics, molecule dynamics, human skeleton motion prediction and pedestrian trajectory prediction. Experimental results show that our method is not only generally applicable, but also achieves state-of-the-art prediction performances on all the four tasks, improving by 24.0/30.1/8.6/9.2%. Code is available at https://github.com/MediaBrain-SJTU/EqMotion. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_EqMotion_Equivariant_Multi-Agent_Motion_Prediction_With_Invariant_Interaction_Reasoning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_EqMotion_Equivariant_Multi-Agent_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.10876 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_EqMotion_Equivariant_Multi-Agent_Motion_Prediction_With_Invariant_Interaction_Reasoning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_EqMotion_Equivariant_Multi-Agent_Motion_Prediction_With_Invariant_Interaction_Reasoning_CVPR_2023_paper.html | CVPR 2023 | null |
Fine-Grained Face Swapping via Regional GAN Inversion | Zhian Liu, Maomao Li, Yong Zhang, Cairong Wang, Qi Zhang, Jue Wang, Yongwei Nie | We present a novel paradigm for high-fidelity face swapping that faithfully preserves the desired subtle geometry and texture details. We rethink face swapping from the perspective of fine-grained face editing, i.e., editing for swapping (E4S), and propose a framework that is based on the explicit disentanglement of the shape and texture of facial components. Following the E4S principle, our framework enables both global and local swapping of facial features, as well as controlling the amount of partial swapping specified by the user. Furthermore, the E4S paradigm is inherently capable of handling facial occlusions by means of facial masks. At the core of our system lies a novel Regional GAN Inversion (RGI) method, which allows the explicit disentanglement of shape and texture. It also allows face swapping to be performed in the latent space of StyleGAN. Specifically, we design a multi-scale mask-guided encoder to project the texture of each facial component into regional style codes. We also design a mask-guided injection module to manipulate the feature maps with the style codes. Based on the disentanglement, face swapping is reformulated as a simplified problem of style and mask swapping. Extensive experiments and comparisons with current state-of-the-art methods demonstrate the superiority of our approach in preserving texture and shape details, as well as working with high resolution images. The project page is https://e4s2022.github.io | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Fine-Grained_Face_Swapping_via_Regional_GAN_Inversion_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Fine-Grained_Face_Swapping_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.14068 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Fine-Grained_Face_Swapping_via_Regional_GAN_Inversion_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Fine-Grained_Face_Swapping_via_Regional_GAN_Inversion_CVPR_2023_paper.html | CVPR 2023 | null |
Taming Diffusion Models for Audio-Driven Co-Speech Gesture Generation | Lingting Zhu, Xian Liu, Xuanyu Liu, Rui Qian, Ziwei Liu, Lequan Yu | Animating virtual avatars to make co-speech gestures facilitates various applications in human-machine interaction. The existing methods mainly rely on generative adversarial networks (GANs), which typically suffer from notorious mode collapse and unstable training, thus making it difficult to learn accurate audio-gesture joint distributions. In this work, we propose a novel diffusion-based framework, named Diffusion Co-Speech Gesture (DiffGesture), to effectively capture the cross-modal audio-to-gesture associations and preserve temporal coherence for high-fidelity audio-driven co-speech gesture generation. Specifically, we first establish the diffusion-conditional generation process on clips of skeleton sequences and audio to enable the whole framework. Then, a novel Diffusion Audio-Gesture Transformer is devised to better attend to the information from multiple modalities and model the long-term temporal dependency. Moreover, to eliminate temporal inconsistency, we propose an effective Diffusion Gesture Stabilizer with an annealed noise sampling strategy. Benefiting from the architectural advantages of diffusion models, we further incorporate implicit classifier-free guidance to trade off between diversity and gesture quality. Extensive experiments demonstrate that DiffGesture achieves state-of-the-art performance, which renders coherent gestures with better mode coverage and stronger audio correlations. Code is available at https://github.com/Advocate99/DiffGesture. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_Taming_Diffusion_Models_for_Audio-Driven_Co-Speech_Gesture_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhu_Taming_Diffusion_Models_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.09119 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_Taming_Diffusion_Models_for_Audio-Driven_Co-Speech_Gesture_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_Taming_Diffusion_Models_for_Audio-Driven_Co-Speech_Gesture_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
FlowFormer++: Masked Cost Volume Autoencoding for Pretraining Optical Flow Estimation | Xiaoyu Shi, Zhaoyang Huang, Dasong Li, Manyuan Zhang, Ka Chun Cheung, Simon See, Hongwei Qin, Jifeng Dai, Hongsheng Li | FlowFormer introduces a transformer architecture into optical flow estimation and achieves state-of-the-art performance. The core component of FlowFormer is the transformer-based cost-volume encoder. Inspired by recent success of masked autoencoding (MAE) pretraining in unleashing transformers' capacity of encoding visual representation, we propose Masked Cost Volume Autoencoding (MCVA) to enhance FlowFormer by pretraining the cost-volume encoder with a novel MAE scheme. Firstly, we introduce a block-sharing masking strategy to prevent masked information leakage, as the cost maps of neighboring source pixels are highly correlated. Secondly, we propose a novel pre-text reconstruction task, which encourages the cost-volume encoder to aggregate long-range information and ensures pretraining-finetuning consistency. We also show how to modify the FlowFormer architecture to accommodate masks during pretraining. Pretrained with MCVA, our proposed FlowFormer++ ranks 1st among published methods on both Sintel and KITTI-2015 benchmarks. Specifically, FlowFormer++ achieves 1.07 and 1.94 average end-point-error (AEPE) on the clean and final pass of Sintel benchmark, leading to 7.76% and 7.18% error reductions from FlowFormer. FlowFormer++ obtains 4.52 F1-all on the KITTI-2015 test set, improving FlowFormer by 0.16. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shi_FlowFormer_Masked_Cost_Volume_Autoencoding_for_Pretraining_Optical_Flow_Estimation_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shi_FlowFormer_Masked_Cost_Volume_Autoencoding_for_Pretraining_Optical_Flow_Estimation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shi_FlowFormer_Masked_Cost_Volume_Autoencoding_for_Pretraining_Optical_Flow_Estimation_CVPR_2023_paper.html | CVPR 2023 | null |
NeRFLix: High-Quality Neural View Synthesis by Learning a Degradation-Driven Inter-Viewpoint MiXer | Kun Zhou, Wenbo Li, Yi Wang, Tao Hu, Nianjuan Jiang, Xiaoguang Han, Jiangbo Lu | Neural radiance fields(NeRF) show great success in novel-view synthesis. However, in real-world scenes, recovering high-quality details from the source images is still challenging for the existing NeRF-based approaches, due to the potential imperfect calibration information and scene representation inaccuracy. Even with high-quality training frames, the synthetic novel-view frames produced by NeRF models still suffer from notable rendering artifacts, such as noise, blur, etc. Towards to improve the synthesis quality of NeRF-based approaches, we propose NeRFLiX, a general NeRF-agnostic restorer paradigm by learning a degradation-driven inter-viewpoint mixer. Specially, we design a NeRF-style degradation modeling approach and construct large-scale training data, enabling the possibility of effectively removing those NeRF-native rendering artifacts for existing deep neural networks. Moreover, beyond the degradation removal, we propose an inter-viewpoint aggregation framework that is able to fuse highly related high-quality training images, pushing the performance of cutting-edge NeRF models to entirely new levels and producing highly photo-realistic synthetic images. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_NeRFLix_High-Quality_Neural_View_Synthesis_by_Learning_a_Degradation-Driven_Inter-Viewpoint_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_NeRFLix_High-Quality_Neural_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.06919 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_NeRFLix_High-Quality_Neural_View_Synthesis_by_Learning_a_Degradation-Driven_Inter-Viewpoint_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_NeRFLix_High-Quality_Neural_View_Synthesis_by_Learning_a_Degradation-Driven_Inter-Viewpoint_CVPR_2023_paper.html | CVPR 2023 | null |
HaLP: Hallucinating Latent Positives for Skeleton-Based Self-Supervised Learning of Actions | Anshul Shah, Aniket Roy, Ketul Shah, Shlok Mishra, David Jacobs, Anoop Cherian, Rama Chellappa | Supervised learning of skeleton sequence encoders for action recognition has received significant attention in recent times. However, learning such encoders without labels continues to be a challenging problem. While prior works have shown promising results by applying contrastive learning to pose sequences, the quality of the learned representations is often observed to be closely tied to data augmentations that are used to craft the positives. However, augmenting pose sequences is a difficult task as the geometric constraints among the skeleton joints need to be enforced to make the augmentations realistic for that action. In this work, we propose a new contrastive learning approach to train models for skeleton-based action recognition without labels. Our key contribution is a simple module, HaLP - to Hallucinate Latent Positives for contrastive learning. Specifically, HaLP explores the latent space of poses in suitable directions to generate new positives. To this end, we present a novel optimization formulation to solve for the synthetic positives with an explicit control on their hardness. We propose approximations to the objective, making them solvable in closed form with minimal overhead. We show via experiments that using these generated positives within a standard contrastive learning framework leads to consistent improvements across benchmarks such as NTU-60, NTU-120, and PKU-II on tasks like linear evaluation, transfer learning, and kNN evaluation. Our code can be found at https://github.com/anshulbshah/HaLP. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shah_HaLP_Hallucinating_Latent_Positives_for_Skeleton-Based_Self-Supervised_Learning_of_Actions_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shah_HaLP_Hallucinating_Latent_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.00387 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shah_HaLP_Hallucinating_Latent_Positives_for_Skeleton-Based_Self-Supervised_Learning_of_Actions_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shah_HaLP_Hallucinating_Latent_Positives_for_Skeleton-Based_Self-Supervised_Learning_of_Actions_CVPR_2023_paper.html | CVPR 2023 | null |
STMixer: A One-Stage Sparse Action Detector | Tao Wu, Mengqi Cao, Ziteng Gao, Gangshan Wu, Limin Wang | Traditional video action detectors typically adopt the two-stage pipeline, where a person detector is first employed to yield actor boxes and then 3D RoIAlign is used to extract actor-specific features for classification. This detection paradigm requires multi-stage training and inference and cannot capture context information outside the bounding box. Recently, a few query-based action detectors are proposed to predict action instances in an end-to-end manner. However, they still lack adaptability in feature sampling or decoding, thus suffering from the issue of inferior performance or slower convergence. In this paper, we propose a new one-stage sparse action detector, termed STMixer. STMixer is based on two core designs. First, we present a query-based adaptive feature sampling module, which endows our STMixer with the flexibility of mining a set of discriminative features from the entire spatiotemporal domain. Second, we devise a dual-branch feature mixing module, which allows our STMixer to dynamically attend to and mix video features along the spatial and the temporal dimension respectively for better feature decoding. Coupling these two designs with a video backbone yields an efficient and accurate action detector. Without bells and whistles, STMixer obtains the state-of-the-art results on the datasets of AVA, UCF101-24, and JHMDB. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_STMixer_A_One-Stage_Sparse_Action_Detector_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_STMixer_A_One-Stage_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15879 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_STMixer_A_One-Stage_Sparse_Action_Detector_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_STMixer_A_One-Stage_Sparse_Action_Detector_CVPR_2023_paper.html | CVPR 2023 | null |
3D Human Keypoints Estimation From Point Clouds in the Wild Without Human Labels | Zhenzhen Weng, Alexander S. Gorban, Jingwei Ji, Mahyar Najibi, Yin Zhou, Dragomir Anguelov | Training a 3D human keypoint detector from point clouds in a supervised manner requires large volumes of high quality labels. While it is relatively easy to capture large amounts of human point clouds, annotating 3D keypoints is expensive, subjective, error prone and especially difficult for long-tail cases (pedestrians with rare poses, scooterists, etc.). In this work, we propose GC-KPL - Geometry Consistency inspired Key Point Leaning, an approach for learning 3D human joint locations from point clouds without human labels. We achieve this by our novel unsupervised loss formulations that account for the structure and movement of the human body. We show that by training on a large training set from Waymo Open Dataset without any human annotated keypoints, we are able to achieve reasonable performance as compared to the fully supervised approach. Further, the backbone benefits from the unsupervised training and is useful in downstream fewshot learning of keypoints, where fine-tuning on only 10 percent of the labeled training data gives comparable performance to fine-tuning on the entire set. We demonstrated that GC-KPL outperforms by a large margin over SoTA when trained on entire dataset and efficiently leverages large volumes of unlabeled data. | https://openaccess.thecvf.com/content/CVPR2023/papers/Weng_3D_Human_Keypoints_Estimation_From_Point_Clouds_in_the_Wild_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Weng_3D_Human_Keypoints_Estimation_From_Point_Clouds_in_the_Wild_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Weng_3D_Human_Keypoints_Estimation_From_Point_Clouds_in_the_Wild_CVPR_2023_paper.html | CVPR 2023 | null |
Where Is My Spot? Few-Shot Image Generation via Latent Subspace Optimization | Chenxi Zheng, Bangzhen Liu, Huaidong Zhang, Xuemiao Xu, Shengfeng He | Image generation relies on massive training data that can hardly produce diverse images of an unseen category according to a few examples. In this paper, we address this dilemma by projecting sparse few-shot samples into a continuous latent space that can potentially generate infinite unseen samples. The rationale behind is that we aim to locate a centroid latent position in a conditional StyleGAN, where the corresponding output image on that centroid can maximize the similarity with the given samples. Although the given samples are unseen for the conditional StyleGAN, we assume the neighboring latent subspace around the centroid belongs to the novel category, and therefore introduce two latent subspace optimization objectives. In the first one we use few-shot samples as positive anchors of the novel class, and adjust the StyleGAN to produce the corresponding results with the new class label condition. The second objective is to govern the generation process from the other way around, by altering the centroid and its surrounding latent subspace for a more precise generation of the novel class. These reciprocal optimization objectives inject a novel class into the StyleGAN latent subspace, and therefore new unseen samples can be easily produced by sampling images from it. Extensive experiments demonstrate superior few-shot generation performances compared with state-of-the-art methods, especially in terms of diversity and generation quality. Code is available at https://github.com/chansey0529/LSO. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zheng_Where_Is_My_Spot_Few-Shot_Image_Generation_via_Latent_Subspace_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zheng_Where_Is_My_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_Where_Is_My_Spot_Few-Shot_Image_Generation_via_Latent_Subspace_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_Where_Is_My_Spot_Few-Shot_Image_Generation_via_Latent_Subspace_CVPR_2023_paper.html | CVPR 2023 | null |
FLEX: Full-Body Grasping Without Full-Body Grasps | Purva Tendulkar, Dídac Surís, Carl Vondrick | Synthesizing 3D human avatars interacting realistically with a scene is an important problem with applications in AR/VR, video games, and robotics. Towards this goal, we address the task of generating a virtual human -- hands and full body -- grasping everyday objects. Existing methods approach this problem by collecting a 3D dataset of humans interacting with objects and training on this data. However, 1) these methods do not generalize to different object positions and orientations or to the presence of furniture in the scene, and 2) the diversity of their generated full-body poses is very limited. In this work, we address all the above challenges to generate realistic, diverse full-body grasps in everyday scenes without requiring any 3D full-body grasping data. Our key insight is to leverage the existence of both full-body pose and hand-grasping priors, composing them using 3D geometrical constraints to obtain full-body grasps. We empirically validate that these constraints can generate a variety of feasible human grasps that are superior to baselines both quantitatively and qualitatively. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tendulkar_FLEX_Full-Body_Grasping_Without_Full-Body_Grasps_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tendulkar_FLEX_Full-Body_Grasping_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tendulkar_FLEX_Full-Body_Grasping_Without_Full-Body_Grasps_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tendulkar_FLEX_Full-Body_Grasping_Without_Full-Body_Grasps_CVPR_2023_paper.html | CVPR 2023 | null |
Genie: Show Me the Data for Quantization | Yongkweon Jeon, Chungman Lee, Ho-young Kim | Zero-shot quantization is a promising approach for developing lightweight deep neural networks when data is inaccessible owing to various reasons, including cost and issues related to privacy. By exploiting the learned parameters (u and sigma) of batch normalization layers in an FP32-pre-trained model, zero-shot quantization schemes focus on generating synthetic data. Subsequently, they distill knowledge from the pre-trained model (teacher) to the quantized model (student) such that the quantized model can be optimized with the synthetic dataset. However, thus far, zero-shot quantization has primarily been discussed in the context of quantization-aware training methods, which require task-specific losses and long-term optimization as much as retraining. We thus introduce a post-training quantization scheme for zero-shot quantization that produces high-quality quantized networks within a few hours. Furthermore, we propose a framework called GENIE that generates data suited for quantization. With the data synthesized by GENIE, we can produce robust quantized models without real datasets, which is comparable to few-shot quantization. We also propose a post-training quantization algorithm to enhance the performance of quantized models. By combining them, we can bridge the gap between zero-shot and few-shot quantization while significantly improving the quantization performance compared to that of existing approaches. In other words, we can obtain a unique state-of-the-art zero-shot quantization approach. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jeon_Genie_Show_Me_the_Data_for_Quantization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jeon_Genie_Show_Me_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.04780 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jeon_Genie_Show_Me_the_Data_for_Quantization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jeon_Genie_Show_Me_the_Data_for_Quantization_CVPR_2023_paper.html | CVPR 2023 | null |
EVA: Exploring the Limits of Masked Visual Representation Learning at Scale | Yuxin Fang, Wen Wang, Binhui Xie, Quan Sun, Ledell Wu, Xinggang Wang, Tiejun Huang, Xinlong Wang, Yue Cao | We launch EVA, a vision-centric foundation model to explore the limits of visual representation at scale using only publicly accessible data. EVA is a vanilla ViT pre-trained to reconstruct the masked out image-text aligned vision features conditioned on visible image patches. Via this pretext task, we can efficiently scale up EVA to one billion parameters, and sets new records on a broad range of representative vision downstream tasks, such as image recognition, video action recognition, object detection, instance segmentation and semantic segmentation without heavy supervised training. Moreover, we observe quantitative changes in scaling EVA result in qualitative changes in transfer learning performance that are not present in other models. For instance, EVA takes a great leap in the challenging large vocabulary instance segmentation task: our model achieves almost the same state-of-the-art performance on LVIS dataset with over a thousand categories and COCO dataset with only eighty categories. Beyond a pure vision encoder, EVA can also serve as a vision-centric, multi-modal pivot to connect images and text. We find initializing the vision tower of a giant CLIP from EVA can greatly stabilize the training and outperform the training from scratch counterpart with much fewer samples and less compute, providing a new direction for scaling up and accelerating the costly training of multi-modal foundation models. | https://openaccess.thecvf.com/content/CVPR2023/papers/Fang_EVA_Exploring_the_Limits_of_Masked_Visual_Representation_Learning_at_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Fang_EVA_Exploring_the_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.07636 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fang_EVA_Exploring_the_Limits_of_Masked_Visual_Representation_Learning_at_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fang_EVA_Exploring_the_Limits_of_Masked_Visual_Representation_Learning_at_CVPR_2023_paper.html | CVPR 2023 | null |
TopNet: Transformer-Based Object Placement Network for Image Compositing | Sijie Zhu, Zhe Lin, Scott Cohen, Jason Kuen, Zhifei Zhang, Chen Chen | We investigate the problem of automatically placing an object into a background image for image compositing. Given a background image and a segmented object, the goal is to train a model to predict plausible placements (location and scale) of the object for compositing. The quality of the composite image highly depends on the predicted location/scale. Existing works either generate candidate bounding boxes or apply sliding-window search using global representations from background and object images, which fail to model local information in background images. However, local clues in background images are important to determine the compatibility of placing the objects with certain locations/scales. In this paper, we propose to learn the correlation between object features and all local background features with a transformer module so that detailed information can be provided on all possible location/scale configurations. A sparse contrastive loss is further proposed to train our model with sparse supervision. Our new formulation generates a 3D heatmap indicating the plausibility of all location/scale combinations in one network forward pass, which is >10x faster than the previous sliding-window method. It also supports interactive search when users provide a pre-defined location or scale. The proposed method can be trained with explicit annotation or in a self-supervised manner using an off-the-shelf inpainting model, and it outperforms state-of-the-art methods significantly. User study shows that the trained model generalizes well to real-world images with diverse challenging scenes and object categories. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_TopNet_Transformer-Based_Object_Placement_Network_for_Image_Compositing_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhu_TopNet_Transformer-Based_Object_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.03372 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_TopNet_Transformer-Based_Object_Placement_Network_for_Image_Compositing_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_TopNet_Transformer-Based_Object_Placement_Network_for_Image_Compositing_CVPR_2023_paper.html | CVPR 2023 | null |
Discrete Point-Wise Attack Is Not Enough: Generalized Manifold Adversarial Attack for Face Recognition | Qian Li, Yuxiao Hu, Ye Liu, Dongxiao Zhang, Xin Jin, Yuntian Chen | Classical adversarial attacks for Face Recognition (FR) models typically generate discrete examples for target identity with a single state image. However, such paradigm of point-wise attack exhibits poor generalization against numerous unknown states of identity and can be easily defended. In this paper, by rethinking the inherent relationship between the face of target identity and its variants, we introduce a new pipeline of Generalized Manifold Adversarial Attack (GMAA) to achieve a better attack performance by expanding the attack range. Specifically, this expansion lies on two aspects -- GMAA not only expands the target to be attacked from one to many to encourage a good generalization ability for the generated adversarial examples, but it also expands the latter from discrete points to manifold by leveraging the domain knowledge that face expression change can be continuous, which enhances the attack effect as a data augmentation mechanism did. Moreover, we further design a dual supervision with local and global constraints as a minor contribution to improve the visual quality of the generated adversarial examples. We demonstrate the effectiveness of our method based on extensive experiments, and reveal that GMAA promises a semantic continuous adversarial space with a higher generalization ability and visual quality. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Discrete_Point-Wise_Attack_Is_Not_Enough_Generalized_Manifold_Adversarial_Attack_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Discrete_Point-Wise_Attack_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.06083 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Discrete_Point-Wise_Attack_Is_Not_Enough_Generalized_Manifold_Adversarial_Attack_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Discrete_Point-Wise_Attack_Is_Not_Enough_Generalized_Manifold_Adversarial_Attack_CVPR_2023_paper.html | CVPR 2023 | null |
Gloss Attention for Gloss-Free Sign Language Translation | Aoxiong Yin, Tianyun Zhong, Li Tang, Weike Jin, Tao Jin, Zhou Zhao | Most sign language translation (SLT) methods to date require the use of gloss annotations to provide additional supervision information, however, the acquisition of gloss is not easy. To solve this problem, we first perform an analysis of existing models to confirm how gloss annotations make SLT easier. We find that it can provide two aspects of information for the model, 1) it can help the model implicitly learn the location of semantic boundaries in continuous sign language videos, 2) it can help the model understand the sign language video globally. We then propose gloss attention, which enables the model to keep its attention within video segments that have the same semantics locally, just as gloss helps existing models do. Furthermore, we transfer the knowledge of sentence-to-sentence similarity from the natural language model to our gloss attention SLT network (GASLT) to help it understand sign language videos at the sentence level. Experimental results on multiple large-scale sign language datasets show that our proposed GASLT model significantly outperforms existing methods. Our code is provided in https://github.com/YinAoXiong/GASLT. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yin_Gloss_Attention_for_Gloss-Free_Sign_Language_Translation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yin_Gloss_Attention_for_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yin_Gloss_Attention_for_Gloss-Free_Sign_Language_Translation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yin_Gloss_Attention_for_Gloss-Free_Sign_Language_Translation_CVPR_2023_paper.html | CVPR 2023 | null |
Multi-Agent Automated Machine Learning | Zhaozhi Wang, Kefan Su, Jian Zhang, Huizhu Jia, Qixiang Ye, Xiaodong Xie, Zongqing Lu | In this paper, we propose multi-agent automated machine learning (MA2ML) with the aim to effectively handle joint optimization of modules in automated machine learning (AutoML). MA2ML takes each machine learning module, such as data augmentation (AUG), neural architecture search (NAS), or hyper-parameters (HPO), as an agent and the final performance as the reward, to formulate a multi-agent reinforcement learning problem. MA2ML explicitly assigns credit to each agent according to its marginal contribution to enhance cooperation among modules, and incorporates off-policy learning to improve search efficiency. Theoretically, MA2ML guarantees monotonic improvement of joint optimization. Extensive experiments show that MA2ML yields the state-of-the-art top-1 accuracy on ImageNet under constraints of computational cost, e.g., 79.7%/80.5% with FLOPs fewer than 600M/800M. Extensive ablation studies verify the benefits of credit assignment and off-policy learning of MA2ML. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Multi-Agent_Automated_Machine_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Multi-Agent_Automated_Machine_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2210.09084 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Multi-Agent_Automated_Machine_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Multi-Agent_Automated_Machine_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Robot Structure Prior Guided Temporal Attention for Camera-to-Robot Pose Estimation From Image Sequence | Yang Tian, Jiyao Zhang, Zekai Yin, Hao Dong | In this work, we tackle the problem of online camera-to-robot pose estimation from single-view successive frames of an image sequence, a crucial task for robots to interact with the world. The primary obstacles of this task are the robot's self-occlusions and the ambiguity of single-view images. This work demonstrates, for the first time, the effectiveness of temporal information and the robot structure prior in addressing these challenges. Given the successive frames and the robot joint configuration, our method learns to accurately regress the 2D coordinates of the predefined robot's keypoints (e.g., joints). With the camera intrinsic and robotic joints status known, we get the camera-to-robot pose using a Perspective-n-point (PnP) solver. We further improve the camera-to-robot pose iteratively using the robot structure prior. To train the whole pipeline, we build a large-scale synthetic dataset generated with domain randomisation to bridge the sim-to-real gap. The extensive experiments on synthetic and real-world datasets and the downstream robotic grasping task demonstrate that our method achieves new state-of-the-art performances and outperforms traditional hand-eye calibration algorithms in real-time (36 FPS). Code and data are available at the project page: https://sites.google.com/view/sgtapose. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tian_Robot_Structure_Prior_Guided_Temporal_Attention_for_Camera-to-Robot_Pose_Estimation_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tian_Robot_Structure_Prior_Guided_Temporal_Attention_for_Camera-to-Robot_Pose_Estimation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tian_Robot_Structure_Prior_Guided_Temporal_Attention_for_Camera-to-Robot_Pose_Estimation_CVPR_2023_paper.html | CVPR 2023 | null |
FREDOM: Fairness Domain Adaptation Approach to Semantic Scene Understanding | Thanh-Dat Truong, Ngan Le, Bhiksha Raj, Jackson Cothren, Khoa Luu | Although Domain Adaptation in Semantic Scene Segmentation has shown impressive improvement in recent years, the fairness concerns in the domain adaptation have yet to be well defined and addressed. In addition, fairness is one of the most critical aspects when deploying the segmentation models into human-related real-world applications, e.g., autonomous driving, as any unfair predictions could influence human safety. In this paper, we propose a novel Fairness Domain Adaptation (FREDOM) approach to semantic scene segmentation. In particular, from the proposed formulated fairness objective, a new adaptation framework will be introduced based on the fair treatment of class distributions. Moreover, to generally model the context of structural dependency, a new conditional structural constraint is introduced to impose the consistency of predicted segmentation. Thanks to the proposed Conditional Structure Network, the self-attention mechanism has sufficiently modeled the structural information of segmentation. Through the ablation studies, the proposed method has shown the performance improvement of the segmentation models and promoted fairness in the model predictions. The experimental results on the two standard benchmarks, i.e., SYNTHIA -> Cityscapes and GTA5 -> Cityscapes, have shown that our method achieved State-of-the-Art (SOTA) performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Truong_FREDOM_Fairness_Domain_Adaptation_Approach_to_Semantic_Scene_Understanding_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Truong_FREDOM_Fairness_Domain_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.02135 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Truong_FREDOM_Fairness_Domain_Adaptation_Approach_to_Semantic_Scene_Understanding_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Truong_FREDOM_Fairness_Domain_Adaptation_Approach_to_Semantic_Scene_Understanding_CVPR_2023_paper.html | CVPR 2023 | null |
IMP: Iterative Matching and Pose Estimation With Adaptive Pooling | Fei Xue, Ignas Budvytis, Roberto Cipolla | Previous methods solve feature matching and pose estimation using a two-stage process by first finding matches and then estimating the pose. As they ignore the geometric relationships between the two tasks, they focus on either improving the quality of matches or filtering potential outliers, leading to limited efficiency or accuracy. In contrast, we propose an iterative matching and pose estimation framework (IMP) leveraging the geometric connections between the two tasks: a few good matches are enough for a roughly accurate pose estimation; a roughly accurate pose can be used to guide the matching by providing geometric constraints. To this end, we implement a geometry-aware recurrent module with transformers which jointly outputs sparse matches and camera poses. Specifically, for each iteration, we first implicitly embed geometric information into the module via a pose-consistency loss, allowing it to predict geometry-aware matches progressively. Second, we introduce an efficient IMP (EIMP) to dynamically discard keypoints without potential matches, avoiding redundant updating and significantly reducing the quadratic time complexity of attention computation in transformers. Experiments on YFCC100m, Scannet, and Aachen Day-Night datasets demonstrate that the proposed method outperforms previous approaches in terms of accuracy and efficiency. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xue_IMP_Iterative_Matching_and_Pose_Estimation_With_Adaptive_Pooling_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xue_IMP_Iterative_Matching_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.14837 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xue_IMP_Iterative_Matching_and_Pose_Estimation_With_Adaptive_Pooling_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xue_IMP_Iterative_Matching_and_Pose_Estimation_With_Adaptive_Pooling_CVPR_2023_paper.html | CVPR 2023 | null |
HRDFuse: Monocular 360deg Depth Estimation by Collaboratively Learning Holistic-With-Regional Depth Distributions | Hao Ai, Zidong Cao, Yan-Pei Cao, Ying Shan, Lin Wang | Depth estimation from a monocular 360 image is a burgeoning problem owing to its holistic sensing of a scene. Recently, some methods, e.g., OmniFusion, have applied the tangent projection (TP) to represent a 360 image and predicted depth values via patch-wise regressions, which are merged to get a depth map with equirectangular projection (ERP) format. However, these methods suffer from 1) non-trivial process of merging a large number of patches; 2) capturing less holistic-with-regional contextual information by directly regressing the depth value of each pixel. In this paper, we propose a novel framework, HRDFuse, that subtly combines the potential of convolutional neural networks (CNNs) and transformers by collaboratively learning the holistic contextual information from the ERP and the regional structural information from the TP. Firstly, we propose a spatial feature alignment (SFA) module that learns feature similarities between the TP and ERP to aggregate the TP features into a complete ERP feature map in a pixel-wise manner. Secondly, we propose a collaborative depth distribution classification (CDDC) module that learns the holistic-with-regional histograms capturing the ERP and TP depth distributions. As such, the final depth values can be predicted as a linear combination of histogram bin centers. Lastly, we adaptively combine the depth predictions from ERP and TP to obtain the final depth map. Extensive experiments show that our method predicts more smooth and accurate depth results while achieving favorably better results than the SOTA methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ai_HRDFuse_Monocular_360deg_Depth_Estimation_by_Collaboratively_Learning_Holistic-With-Regional_Depth_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ai_HRDFuse_Monocular_360deg_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ai_HRDFuse_Monocular_360deg_Depth_Estimation_by_Collaboratively_Learning_Holistic-With-Regional_Depth_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ai_HRDFuse_Monocular_360deg_Depth_Estimation_by_Collaboratively_Learning_Holistic-With-Regional_Depth_CVPR_2023_paper.html | CVPR 2023 | null |
Revisiting Rolling Shutter Bundle Adjustment: Toward Accurate and Fast Solution | Bangyan Liao, Delin Qu, Yifei Xue, Huiqing Zhang, Yizhen Lao | We propose a robust and fast bundle adjustment solution that estimates the 6-DoF pose of the camera and the geometry of the environment based on measurements from a rolling shutter (RS) camera. This tackles the challenges in the existing works, namely relying on additional sensors, high frame rate video as input, restrictive assumptions on camera motion, readout direction, and poor efficiency. To this end, we first investigate the influence of normalization to the image point on RSBA performance and show its better approximation in modelling the real 6-DoF camera motion. Then we present a novel analytical model for the visual residual covariance, which can be used to standardize the reprojection error during the optimization, consequently improving the overall accuracy. More importantly, the combination of normalization and covariance standardization weighting in RSBA (NW-RSBA) can avoid common planar degeneracy without needing to constrain the filming manner. Besides, we propose an acceleration strategy for NW-RSBA based on the sparsity of its Jacobian matrix and Schur complement. The extensive synthetic and real data experiments verify the effectiveness and efficiency of the proposed solution over the state-of-the-art works. We also demonstrate the proposed method can be easily implemented and plug-in famous GSSfM and GSSLAM systems as completed RSSfM and RSSLAM solutions. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liao_Revisiting_Rolling_Shutter_Bundle_Adjustment_Toward_Accurate_and_Fast_Solution_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liao_Revisiting_Rolling_Shutter_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2209.08503 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liao_Revisiting_Rolling_Shutter_Bundle_Adjustment_Toward_Accurate_and_Fast_Solution_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liao_Revisiting_Rolling_Shutter_Bundle_Adjustment_Toward_Accurate_and_Fast_Solution_CVPR_2023_paper.html | CVPR 2023 | null |
StructVPR: Distill Structural Knowledge With Weighting Samples for Visual Place Recognition | Yanqing Shen, Sanping Zhou, Jingwen Fu, Ruotong Wang, Shitao Chen, Nanning Zheng | Visual place recognition (VPR) is usually considered as a specific image retrieval problem. Limited by existing training frameworks, most deep learning-based works cannot extract sufficiently stable global features from RGB images and rely on a time-consuming re-ranking step to exploit spatial structural information for better performance. In this paper, we propose StructVPR, a novel training architecture for VPR, to enhance structural knowledge in RGB global features and thus improve feature stability in a constantly changing environment. Specifically, StructVPR uses segmentation images as a more definitive source of structural knowledge input into a CNN network and applies knowledge distillation to avoid online segmentation and inference of seg-branch in testing. Considering that not all samples contain high-quality and helpful knowledge, and some even hurt the performance of distillation, we partition samples and weigh each sample's distillation loss to enhance the expected knowledge precisely. Finally, StructVPR achieves impressive performance on several benchmarks using only global retrieval and even outperforms many two-stage approaches by a large margin. After adding additional re-ranking, ours achieves state-of-the-art performance while maintaining a low computational cost. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shen_StructVPR_Distill_Structural_Knowledge_With_Weighting_Samples_for_Visual_Place_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shen_StructVPR_Distill_Structural_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.00937 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shen_StructVPR_Distill_Structural_Knowledge_With_Weighting_Samples_for_Visual_Place_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shen_StructVPR_Distill_Structural_Knowledge_With_Weighting_Samples_for_Visual_Place_CVPR_2023_paper.html | CVPR 2023 | null |
PATS: Patch Area Transportation With Subdivision for Local Feature Matching | Junjie Ni, Yijin Li, Zhaoyang Huang, Hongsheng Li, Hujun Bao, Zhaopeng Cui, Guofeng Zhang | Local feature matching aims at establishing sparse correspondences between a pair of images. Recently, detector-free methods present generally better performance but are not satisfactory in image pairs with large scale differences. In this paper, we propose Patch Area Transportation with Subdivision (PATS) to tackle this issue. Instead of building an expensive image pyramid, we start by splitting the original image pair into equal-sized patches and gradually resizing and subdividing them into smaller patches with the same scale. However, estimating scale differences between these patches is non-trivial since the scale differences are determined by both relative camera poses and scene structures, and thus spatially varying over image pairs. Moreover, it is hard to obtain the ground truth for real scenes. To this end, we propose patch area transportation, which enables learning scale differences in a self-supervised manner. In contrast to bipartite graph matching, which only handles one-to-one matching, our patch area transportation can deal with many-to-many relationships. PATS improves both matching accuracy and coverage, and shows superior performance in downstream tasks, such as relative pose estimation, visual localization, and optical flow estimation.The source code will be released to benefit the community. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ni_PATS_Patch_Area_Transportation_With_Subdivision_for_Local_Feature_Matching_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ni_PATS_Patch_Area_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.07700 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ni_PATS_Patch_Area_Transportation_With_Subdivision_for_Local_Feature_Matching_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ni_PATS_Patch_Area_Transportation_With_Subdivision_for_Local_Feature_Matching_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Human-to-Robot Handovers From Point Clouds | Sammy Christen, Wei Yang, Claudia Pérez-D’Arpino, Otmar Hilliges, Dieter Fox, Yu-Wei Chao | We propose the first framework to learn control policies for vision-based human-to-robot handovers, a critical task for human-robot interaction. While research in Embodied AI has made significant progress in training robot agents in simulated environments, interacting with humans remains challenging due to the difficulties of simulating humans. Fortunately, recent research has developed realistic simulated environments for human-to-robot handovers. Leveraging this result, we introduce a method that is trained with a human-in-the-loop via a two-stage teacher-student framework that uses motion and grasp planning, reinforcement learning, and self-supervision. We show significant performance gains over baselines on a simulation benchmark, sim-to-sim transfer and sim-to-real transfer. | https://openaccess.thecvf.com/content/CVPR2023/papers/Christen_Learning_Human-to-Robot_Handovers_From_Point_Clouds_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Christen_Learning_Human-to-Robot_Handovers_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Christen_Learning_Human-to-Robot_Handovers_From_Point_Clouds_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Christen_Learning_Human-to-Robot_Handovers_From_Point_Clouds_CVPR_2023_paper.html | CVPR 2023 | null |
MEDIC: Remove Model Backdoors via Importance Driven Cloning | Qiuling Xu, Guanhong Tao, Jean Honorio, Yingqi Liu, Shengwei An, Guangyu Shen, Siyuan Cheng, Xiangyu Zhang | We develop a novel method to remove injected backdoors in deep learning models. It works by cloning the benign behaviors of a trojaned model to a new model of the same structure. It trains the clone model from scratch on a very small subset of samples and aims to minimize a cloning loss that denotes the differences between the activations of important neurons across the two models. The set of important neurons varies for each input, depending on their magnitude of activations and their impact on the classification result. We theoretically show our method can better recover benign functions of the backdoor model. Meanwhile, we prove our method can be more effective in removing backdoors compared with fine-tuning. Our experiments show that our technique can effectively remove nine different types of backdoors with minor benign accuracy degradation, outperforming the state-of-the-art backdoor removal techniques that are based on fine-tuning, knowledge distillation, and neuron pruning. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_MEDIC_Remove_Model_Backdoors_via_Importance_Driven_Cloning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_MEDIC_Remove_Model_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_MEDIC_Remove_Model_Backdoors_via_Importance_Driven_Cloning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_MEDIC_Remove_Model_Backdoors_via_Importance_Driven_Cloning_CVPR_2023_paper.html | CVPR 2023 | null |
Context-Aware Relative Object Queries To Unify Video Instance and Panoptic Segmentation | Anwesa Choudhuri, Girish Chowdhary, Alexander G. Schwing | Object queries have emerged as a powerful abstraction to generically represent object proposals. However, their use for temporal tasks like video segmentation poses two questions: 1) How to process frames sequentially and propagate object queries seamlessly across frames. Using independent object queries per frame doesn't permit tracking, and requires post-processing. 2) How to produce temporally consistent, yet expressive object queries that model both appearance and position changes. Using the entire video at once doesn't capture position changes and doesn't scale to long videos. As one answer to both questions we propose 'context-aware relative object queries', which are continuously propagated frame-by-frame. They seamlessly track objects and deal with occlusion and re-appearance of objects, without post-processing. Further, we find context-aware relative object queries better capture position changes of objects in motion. We evaluate the proposed approach across three challenging tasks: video instance segmentation, multi-object tracking and segmentation, and video panoptic segmentation. Using the same approach and architecture, we match or surpass state-of-the art results on the diverse and challenging OVIS, Youtube-VIS, Cityscapes-VPS, MOTS 2020 and KITTI-MOTS data. | https://openaccess.thecvf.com/content/CVPR2023/papers/Choudhuri_Context-Aware_Relative_Object_Queries_To_Unify_Video_Instance_and_Panoptic_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Choudhuri_Context-Aware_Relative_Object_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Choudhuri_Context-Aware_Relative_Object_Queries_To_Unify_Video_Instance_and_Panoptic_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Choudhuri_Context-Aware_Relative_Object_Queries_To_Unify_Video_Instance_and_Panoptic_CVPR_2023_paper.html | CVPR 2023 | null |
Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D Generation | Haochen Wang, Xiaodan Du, Jiahao Li, Raymond A. Yeh, Greg Shakhnarovich | A diffusion model learns to predict a vector field of gradients. We propose to apply chain rule on the learned gradients, and back-propagate the score of a diffusion model through the Jacobian of a differentiable renderer, which we instantiate to be a voxel radiance field. This setup aggregates 2D scores at multiple camera viewpoints into a 3D score, and repurposes a pretrained 2D model for 3D data generation. We identify a technical challenge of distribution mismatch that arises in this application, and propose a novel estimation mechanism to resolve it. We run our algorithm on several off-the-shelf diffusion image generative models, including the recently released Stable Diffusion trained on the large-scale LAION dataset. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Score_Jacobian_Chaining_Lifting_Pretrained_2D_Diffusion_Models_for_3D_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Score_Jacobian_Chaining_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.00774 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Score_Jacobian_Chaining_Lifting_Pretrained_2D_Diffusion_Models_for_3D_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Score_Jacobian_Chaining_Lifting_Pretrained_2D_Diffusion_Models_for_3D_CVPR_2023_paper.html | CVPR 2023 | null |
Role of Transients in Two-Bounce Non-Line-of-Sight Imaging | Siddharth Somasundaram, Akshat Dave, Connor Henley, Ashok Veeraraghavan, Ramesh Raskar | The goal of non-line-of-sight (NLOS) imaging is to image objects occluded from the camera's field of view using multiply scattered light. Recent works have demonstrated the feasibility of two-bounce (2B) NLOS imaging by scanning a laser and measuring cast shadows of occluded objects in scenes with two relay surfaces. In this work, we study the role of time-of-flight (ToF) measurements, i.e. transients, in 2B-NLOS under multiplexed illumination. Specifically, we study how ToF information can reduce the number of measurements and spatial resolution needed for shape reconstruction. We present our findings with respect to tradeoffs in (1) temporal resolution, (2) spatial resolution, and (3) number of image captures by studying SNR and recoverability as functions of system parameters. This leads to a formal definition of the mathematical constraints for 2B lidar. We believe that our work lays an analytical groundwork for design of future NLOS imaging systems, especially as ToF sensors become increasingly ubiquitous. | https://openaccess.thecvf.com/content/CVPR2023/papers/Somasundaram_Role_of_Transients_in_Two-Bounce_Non-Line-of-Sight_Imaging_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Somasundaram_Role_of_Transients_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.01308 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Somasundaram_Role_of_Transients_in_Two-Bounce_Non-Line-of-Sight_Imaging_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Somasundaram_Role_of_Transients_in_Two-Bounce_Non-Line-of-Sight_Imaging_CVPR_2023_paper.html | CVPR 2023 | null |
SimpleNet: A Simple Network for Image Anomaly Detection and Localization | Zhikang Liu, Yiming Zhou, Yuansheng Xu, Zilei Wang | We propose a simple and application-friendly network (called SimpleNet) for detecting and localizing anomalies. SimpleNet consists of four components: (1) a pre-trained Feature Extractor that generates local features, (2) a shallow Feature Adapter that transfers local features towards target domain, (3) a simple Anomaly Feature Generator that counterfeits anomaly features by adding Gaussian noise to normal features, and (4) a binary Anomaly Discriminator that distinguishes anomaly features from normal features. During inference, the Anomaly Feature Generator would be discarded. Our approach is based on three intuitions. First, transforming pre-trained features to target-oriented features helps avoid domain bias. Second, generating synthetic anomalies in feature space is more effective, as defects may not have much commonality in the image space. Third, a simple discriminator is much efficient and practical. In spite of simplicity, SimpleNet outperforms previous methods quantitatively and qualitatively. On the MVTec AD benchmark, SimpleNet achieves an anomaly detection AUROC of 99.6%, reducing the error by 55.5% compared to the next best performing model. Furthermore, SimpleNet is faster than existing methods, with a high frame rate of 77 FPS on a 3080ti GPU. Additionally, SimpleNet demonstrates significant improvements in performance on the One-Class Novelty Detection task. Code: https://github.com/DonaldRR/SimpleNet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_SimpleNet_A_Simple_Network_for_Image_Anomaly_Detection_and_Localization_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.15140 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_SimpleNet_A_Simple_Network_for_Image_Anomaly_Detection_and_Localization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_SimpleNet_A_Simple_Network_for_Image_Anomaly_Detection_and_Localization_CVPR_2023_paper.html | CVPR 2023 | null |
Elastic Aggregation for Federated Optimization | Dengsheng Chen, Jie Hu, Vince Junkai Tan, Xiaoming Wei, Enhua Wu | Federated learning enables the privacy-preserving training of neural network models using real-world data across distributed clients. FedAvg has become the preferred optimizer for federated learning because of its simplicity and effectiveness. FedAvg uses naive aggregation to update the server model, interpolating client models based on the number of instances used in their training. However, naive aggregation suffers from client-drift when the data is heterogenous (non-IID), leading to unstable and slow convergence. In this work, we propose a novel aggregation approach, elastic aggregation, to overcome these issues. Elastic aggregation interpolates client models adaptively according to parameter sensitivity, which is measured by computing how much the overall prediction function output changes when each parameter is changed. This measurement is performed in an unsupervised and online manner. Elastic aggregation reduces the magnitudes of updates to the more sensitive parameters so as to prevent the server model from drifting to any one client distribution, and conversely boosts updates to the less sensitive parameters to better explore different client distributions. Empirical results on real and synthetic data as well as analytical results show that elastic aggregation leads to efficient training in both convex and non-convex settings, while being fully agnostic to client heterogeneity and robust to large numbers of clients, partial participation, and imbalanced data. Finally, elastic aggregation works well with other federated optimizers and achieves significant improvements across the board. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Elastic_Aggregation_for_Federated_Optimization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Elastic_Aggregation_for_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Elastic_Aggregation_for_Federated_Optimization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Elastic_Aggregation_for_Federated_Optimization_CVPR_2023_paper.html | CVPR 2023 | null |
G-MSM: Unsupervised Multi-Shape Matching With Graph-Based Affinity Priors | Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé, Daniel Cremers | We present G-MSM (Graph-based Multi-Shape Matching), a novel unsupervised learning approach for non-rigid shape correspondence. Rather than treating a collection of input poses as an unordered set of samples, we explicitly model the underlying shape data manifold. To this end, we propose an adaptive multi-shape matching architecture that constructs an affinity graph on a given set of training shapes in a self-supervised manner. The key idea is to combine putative, pairwise correspondences by propagating maps along shortest paths in the underlying shape graph. During training, we enforce cycle-consistency between such optimal paths and the pairwise matches which enables our model to learn topology-aware shape priors. We explore different classes of shape graphs and recover specific settings, like template-based matching (star graph) or learnable ranking/sorting (TSP graph), as special cases in our framework. Finally, we demonstrate state-of-the-art performance on several recent shape correspondence benchmarks, including real-world 3D scan meshes with topological noise and challenging inter-class pairs. | https://openaccess.thecvf.com/content/CVPR2023/papers/Eisenberger_G-MSM_Unsupervised_Multi-Shape_Matching_With_Graph-Based_Affinity_Priors_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Eisenberger_G-MSM_Unsupervised_Multi-Shape_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Eisenberger_G-MSM_Unsupervised_Multi-Shape_Matching_With_Graph-Based_Affinity_Priors_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Eisenberger_G-MSM_Unsupervised_Multi-Shape_Matching_With_Graph-Based_Affinity_Priors_CVPR_2023_paper.html | CVPR 2023 | null |
Enhancing Deformable Local Features by Jointly Learning To Detect and Describe Keypoints | Guilherme Potje, Felipe Cadar, André Araujo, Renato Martins, Erickson R. Nascimento | Local feature extraction is a standard approach in computer vision for tackling important tasks such as image matching and retrieval. The core assumption of most methods is that images undergo affine transformations, disregarding more complicated effects such as non-rigid deformations. Furthermore, incipient works tailored for non-rigid correspondence still rely on keypoint detectors designed for rigid transformations, hindering performance due to the limitations of the detector. We propose DALF (Deformation-Aware Local Features), a novel deformation-aware network for jointly detecting and describing keypoints, to handle the challenging problem of matching deformable surfaces. All network components work cooperatively through a feature fusion approach that enforces the descriptors' distinctiveness and invariance. Experiments using real deforming objects showcase the superiority of our method, where it delivers 8% improvement in matching scores compared to the previous best results. Our approach also enhances the performance of two real-world applications: deformable object retrieval and non-rigid 3D surface registration. Code for training, inference, and applications are publicly available at verlab.dcc.ufmg.br/descriptors/dalf_cvpr23. | https://openaccess.thecvf.com/content/CVPR2023/papers/Potje_Enhancing_Deformable_Local_Features_by_Jointly_Learning_To_Detect_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Potje_Enhancing_Deformable_Local_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2304.00583 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Potje_Enhancing_Deformable_Local_Features_by_Jointly_Learning_To_Detect_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Potje_Enhancing_Deformable_Local_Features_by_Jointly_Learning_To_Detect_and_CVPR_2023_paper.html | CVPR 2023 | null |
ObjectMatch: Robust Registration Using Canonical Object Correspondences | Can Gümeli, Angela Dai, Matthias Nießner | We present ObjectMatch, a semantic and object-centric camera pose estimator for RGB-D SLAM pipelines. Modern camera pose estimators rely on direct correspondences of overlapping regions between frames; however, they cannot align camera frames with little or no overlap. In this work, we propose to leverage indirect correspondences obtained via semantic object identification. For instance, when an object is seen from the front in one frame and from the back in another frame, we can provide additional pose constraints through canonical object correspondences. We first propose a neural network to predict such correspondences on a per-pixel level, which we then combine in our energy formulation with state-of-the-art keypoint matching solved with a joint Gauss-Newton optimization. In a pairwise setting, our method improves registration recall of state-of-the-art feature matching, including from 24% to 45% in pairs with 10% or less inter-frame overlap. In registering RGB-D sequences, our method outperforms cutting-edge SLAM baselines in challenging, low-frame-rate scenarios, achieving more than 35% reduction in trajectory error in multiple scenes. | https://openaccess.thecvf.com/content/CVPR2023/papers/Gumeli_ObjectMatch_Robust_Registration_Using_Canonical_Object_Correspondences_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gumeli_ObjectMatch_Robust_Registration_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gumeli_ObjectMatch_Robust_Registration_Using_Canonical_Object_Correspondences_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gumeli_ObjectMatch_Robust_Registration_Using_Canonical_Object_Correspondences_CVPR_2023_paper.html | CVPR 2023 | null |
Siamese Image Modeling for Self-Supervised Vision Representation Learning | Chenxin Tao, Xizhou Zhu, Weijie Su, Gao Huang, Bin Li, Jie Zhou, Yu Qiao, Xiaogang Wang, Jifeng Dai | Self-supervised learning (SSL) has delivered superior performance on a variety of downstream vision tasks. Two main-stream SSL frameworks have been proposed, i.e., Instance Discrimination (ID) and Masked Image Modeling (MIM). ID pulls together representations from different views of the same image, while avoiding feature collapse. It lacks spatial sensitivity, which requires modeling the local structure within each image. On the other hand, MIM reconstructs the original content given a masked image. It instead does not have good semantic alignment, which requires projecting semantically similar views into nearby representations. To address this dilemma, we observe that (1) semantic alignment can be achieved by matching different image views with strong augmentations; (2) spatial sensitivity can benefit from predicting dense representations with masked images. Driven by these analysis, we propose Siamese Image Modeling (SiameseIM), which predicts the dense representations of an augmented view, based on another masked view from the same image but with different augmentations. SiameseIM uses a Siamese network with two branches. The online branch encodes the first view, and predicts the second view's representation according to the relative positions between these two views. The target branch produces the target by encoding the second view. SiameseIM can surpass both ID and MIM on a wide range of downstream tasks, including ImageNet finetuning and linear probing, COCO and LVIS detection, and ADE20k semantic segmentation. The improvement is more significant in few-shot, long-tail and robustness-concerned scenarios. Code shall be released. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tao_Siamese_Image_Modeling_for_Self-Supervised_Vision_Representation_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tao_Siamese_Image_Modeling_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2206.01204 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tao_Siamese_Image_Modeling_for_Self-Supervised_Vision_Representation_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tao_Siamese_Image_Modeling_for_Self-Supervised_Vision_Representation_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Generating Part-Aware Editable 3D Shapes Without 3D Supervision | Konstantinos Tertikas, Despoina Paschalidou, Boxiao Pan, Jeong Joon Park, Mikaela Angelina Uy, Ioannis Emiris, Yannis Avrithis, Leonidas Guibas | Impressive progress in generative models and implicit representations gave rise to methods that can generate 3D shapes of high quality. However, being able to locally control and edit shapes is another essential property that can unlock several content creation applications. Local control can be achieved with part-aware models, but existing methods require 3D supervision and cannot produce textures. In this work, we devise PartNeRF, a novel part-aware generative model for editable 3D shape synthesis that does not require any explicit 3D supervision. Our model generates objects as a set of locally defined NeRFs, augmented with an affine transformation. This enables several editing operations such as applying transformations on parts, mixing parts from different objects etc. To ensure distinct, manipulable parts we enforce a hard assignment of rays to parts that makes sure that the color of each ray is only determined by a single NeRF. As a result, altering one part does not affect the appearance of the others. Evaluations on various ShapeNet categories demonstrate the ability of our model to generate editable 3D objects of improved fidelity, compared to previous part-based generative approaches that require 3D supervision or models relying on NeRFs. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tertikas_Generating_Part-Aware_Editable_3D_Shapes_Without_3D_Supervision_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tertikas_Generating_Part-Aware_Editable_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.09554 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tertikas_Generating_Part-Aware_Editable_3D_Shapes_Without_3D_Supervision_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tertikas_Generating_Part-Aware_Editable_3D_Shapes_Without_3D_Supervision_CVPR_2023_paper.html | CVPR 2023 | null |
Center Focusing Network for Real-Time LiDAR Panoptic Segmentation | Xiaoyan Li, Gang Zhang, Boyue Wang, Yongli Hu, Baocai Yin | LiDAR panoptic segmentation facilitates an autonomous vehicle to comprehensively understand the surrounding objects and scenes and is required to run in real time. The recent proposal-free methods accelerate the algorithm, but their effectiveness and efficiency are still limited owing to the difficulty of modeling non-existent instance centers and the costly center-based clustering modules. To achieve accurate and real-time LiDAR panoptic segmentation, a novel center focusing network (CFNet) is introduced. Specifically, the center focusing feature encoding (CFFE) is proposed to explicitly understand the relationships between the original LiDAR points and virtual instance centers by shifting the LiDAR points and filling in the center points. Moreover, to leverage the redundantly detected centers, a fast center deduplication module (CDM) is proposed to select only one center for each instance. Experiments on the SemanticKITTI and nuScenes panoptic segmentation benchmarks demonstrate that our CFNet outperforms all existing methods by a large margin and is 1.6 times faster than the most efficient method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Center_Focusing_Network_for_Real-Time_LiDAR_Panoptic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Center_Focusing_Network_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Center_Focusing_Network_for_Real-Time_LiDAR_Panoptic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Center_Focusing_Network_for_Real-Time_LiDAR_Panoptic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
High-Fidelity Facial Avatar Reconstruction From Monocular Video With Generative Priors | Yunpeng Bai, Yanbo Fan, Xuan Wang, Yong Zhang, Jingxiang Sun, Chun Yuan, Ying Shan | High-fidelity facial avatar reconstruction from a monocular video is a significant research problem in computer graphics and computer vision. Recently, Neural Radiance Field (NeRF) has shown impressive novel view rendering results and has been considered for facial avatar reconstruction. However, the complex facial dynamics and missing 3D information in monocular videos raise significant challenges for faithful facial reconstruction. In this work, we propose a new method for NeRF-based facial avatar reconstruction that utilizes 3D-aware generative prior. Different from existing works that depend on a conditional deformation field for dynamic modeling, we propose to learn a personalized generative prior, which is formulated as a local and low dimensional subspace in the latent space of 3D-GAN. We propose an efficient method to construct the personalized generative prior based on a small set of facial images of a given individual. After learning, it allows for photo-realistic rendering with novel views, and the face reenactment can be realized by performing navigation in the latent space. Our proposed method is applicable for different driven signals, including RGB images, 3DMM coefficients, and audio. Compared with existing works, we obtain superior novel view synthesis results and faithfully face reenactment performance. The code is available here https://github.com/bbaaii/HFA-GP. | https://openaccess.thecvf.com/content/CVPR2023/papers/Bai_High-Fidelity_Facial_Avatar_Reconstruction_From_Monocular_Video_With_Generative_Priors_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bai_High-Fidelity_Facial_Avatar_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.15064 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bai_High-Fidelity_Facial_Avatar_Reconstruction_From_Monocular_Video_With_Generative_Priors_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bai_High-Fidelity_Facial_Avatar_Reconstruction_From_Monocular_Video_With_Generative_Priors_CVPR_2023_paper.html | CVPR 2023 | null |
Mixed Autoencoder for Self-Supervised Visual Representation Learning | Kai Chen, Zhili Liu, Lanqing Hong, Hang Xu, Zhenguo Li, Dit-Yan Yeung | Masked Autoencoder (MAE) has demonstrated superior performance on various vision tasks via randomly masking image patches and reconstruction. However, effective data augmentation strategies for MAE still remain open questions, different from those in contrastive learning that serve as the most important part. This paper studies the prevailing mixing augmentation for MAE. We first demonstrate that naive mixing will in contrast degenerate model performance due to the increase of mutual information (MI). To address, we propose homologous recognition, an auxiliary pretext task, not only to alleviate the MI increasement by explicitly requiring each patch to recognize homologous patches, but also to perform object-aware self-supervised pre-training for better downstream dense perception performance. With extensive experiments, we demonstrate that our proposed Mixed Autoencoder (MixedAE) achieves the state-of-the-art transfer results among masked image modeling (MIM) augmentations on different downstream tasks with significant efficiency. Specifically, our MixedAE outperforms MAE by +0.3% accuracy, +1.7 mIoU and +0.9 AP on ImageNet-1K, ADE20K and COCO respectively with a standard ViT-Base. Moreover, MixedAE surpasses iBOT, a strong MIM method combined with instance discrimination, while accelerating training by 2x. To our best knowledge, this is the very first work to consider mixing for MIM from the perspective of pretext task design. Code will be made available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Mixed_Autoencoder_for_Self-Supervised_Visual_Representation_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Mixed_Autoencoder_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.17152 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Mixed_Autoencoder_for_Self-Supervised_Visual_Representation_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Mixed_Autoencoder_for_Self-Supervised_Visual_Representation_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Restoration of Hand-Drawn Architectural Drawings Using Latent Space Mapping With Degradation Generator | Nakkwan Choi, Seungjae Lee, Yongsik Lee, Seungjoon Yang | This work presents the restoration of drawings of wooden built heritage. Hand-drawn drawings contain the most important original information but are often severely degraded over time. A novel restoration method based on the vector quantized variational autoencoders is presented. Latent space representations of drawings and noise are learned, which are used to map noisy drawings to clean drawings for restoration and to generate authentic noisy drawings for data augmentation. The proposed method is applied to the drawings archived in the Cultural Heritage Administration. Restored drawings show significant quality improvement and allow more accurate interpretations of information. | https://openaccess.thecvf.com/content/CVPR2023/papers/Choi_Restoration_of_Hand-Drawn_Architectural_Drawings_Using_Latent_Space_Mapping_With_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Choi_Restoration_of_Hand-Drawn_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Choi_Restoration_of_Hand-Drawn_Architectural_Drawings_Using_Latent_Space_Mapping_With_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Choi_Restoration_of_Hand-Drawn_Architectural_Drawings_Using_Latent_Space_Mapping_With_CVPR_2023_paper.html | CVPR 2023 | null |
CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network With Large Input | Senmao Tian, Ming Lu, Jiaming Liu, Yandong Guo, Yurong Chen, Shunli Zhang | With the development of high-definition display devices, the practical scenario of Super-Resolution (SR) usually needs to super-resolve large input like 2K to higher resolution (4K/8K). To reduce the computational and memory cost, current methods first split the large input into local patches and then merge the SR patches into the output. These methods adaptively allocate a subnet for each patch. Quantization is a very important technique for network acceleration and has been used to design the subnets. Current methods train an MLP bit selector to determine the propoer bit for each layer. However, they uniformly sample subnets for training, making simple subnets overfitted and complicated subnets underfitted. Therefore, the trained bit selector fails to determine the optimal bit. Apart from this, the introduced bit selector brings additional cost to each layer of the SR network. In this paper, we propose a novel method named Content-Aware Bit Mapping (CABM), which can remove the bit selector without any performance loss. CABM also learns a bit selector for each layer during training. After training, we analyze the relation between the edge information of an input patch and the bit of each layer. We observe that the edge information can be an effective metric for the selected bit. Therefore, we design a strategy to build an Edge-to-Bit lookup table that maps the edge score of a patch to the bit of each layer during inference. The bit configuration of SR network can be determined by the lookup tables of all layers. Our strategy can find better bit configuration, resulting in more efficient mixed precision networks. We conduct detailed experiments to demonstrate the generalization ability of our method. The code will be released. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tian_CABM_Content-Aware_Bit_Mapping_for_Single_Image_Super-Resolution_Network_With_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2304.06454 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tian_CABM_Content-Aware_Bit_Mapping_for_Single_Image_Super-Resolution_Network_With_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tian_CABM_Content-Aware_Bit_Mapping_for_Single_Image_Super-Resolution_Network_With_CVPR_2023_paper.html | CVPR 2023 | null |
Decoupling MaxLogit for Out-of-Distribution Detection | Zihan Zhang, Xiang Xiang | In machine learning, it is often observed that standard training outputs anomalously high confidence for both in-distribution (ID) and out-of-distribution (OOD) data. Thus, the ability to detect OOD samples is critical to the model deployment. An essential step for OOD detection is post-hoc scoring. MaxLogit is one of the simplest scoring functions which uses the maximum logits as OOD score. To provide a new viewpoint to study the logit-based scoring function, we reformulate the logit into cosine similarity and logit norm and propose to use MaxCosine and MaxNorm. We empirically find that MaxCosine is a core factor in the effectiveness of MaxLogit. And the performance of MaxLogit is encumbered by MaxNorm. To tackle the problem, we propose the Decoupling MaxLogit (DML) for flexibility to balance MaxCosine and MaxNorm. To further embody the core of our method, we extend DML to DML+ based on the new insights that fewer hard samples and compact feature space are the key components to make logit-based methods effective. We demonstrate the effectiveness of our logit-based OOD detection methods on CIFAR-10, CIFAR-100 and ImageNet and establish state-of-the-art performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Decoupling_MaxLogit_for_Out-of-Distribution_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Decoupling_MaxLogit_for_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Decoupling_MaxLogit_for_Out-of-Distribution_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Decoupling_MaxLogit_for_Out-of-Distribution_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
ProphNet: Efficient Agent-Centric Motion Forecasting With Anchor-Informed Proposals | Xishun Wang, Tong Su, Fang Da, Xiaodong Yang | Motion forecasting is a key module in an autonomous driving system. Due to the heterogeneous nature of multi-sourced input, multimodality in agent behavior, and low latency required by onboard deployment, this task is notoriously challenging. To cope with these difficulties, this paper proposes a novel agent-centric model with anchor-informed proposals for efficient multimodal motion forecasting. We design a modality-agnostic strategy to concisely encode the complex input in a unified manner. We generate diverse proposals, fused with anchors bearing goal-oriented context, to induce multimodal prediction that covers a wide range of future trajectories. The network architecture is highly uniform and succinct, leading to an efficient model amenable for real-world deployment. Experiments reveal that our agent-centric network compares favorably with the state-of-the-art methods in prediction accuracy, while achieving scene-centric level inference latency. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_ProphNet_Efficient_Agent-Centric_Motion_Forecasting_With_Anchor-Informed_Proposals_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_ProphNet_Efficient_Agent-Centric_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.12071 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_ProphNet_Efficient_Agent-Centric_Motion_Forecasting_With_Anchor-Informed_Proposals_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_ProphNet_Efficient_Agent-Centric_Motion_Forecasting_With_Anchor-Informed_Proposals_CVPR_2023_paper.html | CVPR 2023 | null |
Generalizing Dataset Distillation via Deep Generative Prior | George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A. Efros, Jun-Yan Zhu | Dataset Distillation aims to distill an entire dataset's knowledge into a few synthetic images. The idea is to synthesize a small number of synthetic data points that, when given to a learning algorithm as training data, result in a model approximating one trained on the original data. Despite a recent upsurge of progress in the field, existing dataset distillation methods fail to generalize to new architectures and scale to high-resolution datasets. To overcome the above issues, we propose to use the learned prior from pre-trained deep generative models to synthesize the distilled data. To achieve this, we present a new optimization algorithm that distills a large number of images into a few intermediate feature vectors in the generative model's latent space. Our method augments existing techniques, significantly improving cross-architecture generalization in all settings. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cazenavette_Generalizing_Dataset_Distillation_via_Deep_Generative_Prior_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cazenavette_Generalizing_Dataset_Distillation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.01649 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cazenavette_Generalizing_Dataset_Distillation_via_Deep_Generative_Prior_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cazenavette_Generalizing_Dataset_Distillation_via_Deep_Generative_Prior_CVPR_2023_paper.html | CVPR 2023 | null |
Few-Shot Class-Incremental Learning via Class-Aware Bilateral Distillation | Linglan Zhao, Jing Lu, Yunlu Xu, Zhanzhan Cheng, Dashan Guo, Yi Niu, Xiangzhong Fang | Few-Shot Class-Incremental Learning (FSCIL) aims to continually learn novel classes based on only few training samples, which poses a more challenging task than the well-studied Class-Incremental Learning (CIL) due to data scarcity. While knowledge distillation, a prevailing technique in CIL, can alleviate the catastrophic forgetting of older classes by regularizing outputs between current and previous model, it fails to consider the overfitting risk of novel classes in FSCIL. To adapt the powerful distillation technique for FSCIL, we propose a novel distillation structure, by taking the unique challenge of overfitting into account. Concretely, we draw knowledge from two complementary teachers. One is the model trained on abundant data from base classes that carries rich general knowledge, which can be leveraged for easing the overfitting of current novel classes. The other is the updated model from last incremental session that contains the adapted knowledge of previous novel classes, which is used for alleviating their forgetting. To combine the guidances, an adaptive strategy conditioned on the class-wise semantic similarities is introduced. Besides, for better preserving base class knowledge when accommodating novel concepts, we adopt a two-branch network with an attention-based aggregation module to dynamically merge predictions from two complementary branches. Extensive experiments on 3 popular FSCIL datasets: mini-ImageNet, CIFAR100 and CUB200 validate the effectiveness of our method by surpassing existing works by a significant margin. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhao_Few-Shot_Class-Incremental_Learning_via_Class-Aware_Bilateral_Distillation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhao_Few-Shot_Class-Incremental_Learning_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Few-Shot_Class-Incremental_Learning_via_Class-Aware_Bilateral_Distillation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Few-Shot_Class-Incremental_Learning_via_Class-Aware_Bilateral_Distillation_CVPR_2023_paper.html | CVPR 2023 | null |
Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo | Yuesong Wang, Zhaojie Zeng, Tao Guan, Wei Yang, Zhuo Chen, Wenkai Liu, Luoyuan Xu, Yawei Luo | In recent years, deep learning-based approaches have shown great strength in multi-view stereo because of their outstanding ability to extract robust visual features. However, most learning-based methods need to build the cost volume and increase the receptive field enormously to get a satisfactory result when dealing with large-scale textureless regions, consequently leading to prohibitive memory consumption. To ensure both memory-friendly and textureless-resilient, we innovatively transplant the spirit of deformable convolution from deep learning into the traditional PatchMatch-based method. Specifically, for each pixel with matching ambiguity (termed unreliable pixel), we adaptively deform the patch centered on it to extend the receptive field until covering enough correlative reliable pixels (without matching ambiguity) that serve as anchors. When performing PatchMatch, constrained by the anchor pixels, the matching cost of an unreliable pixel is guaranteed to reach the global minimum at the correct depth and therefore increases the robustness of multi-view stereo significantly. To detect more anchor pixels to ensure better adaptive patch deformation, we propose to evaluate the matching ambiguity of a certain pixel by checking the convergence of the estimated depth as optimization proceeds. As a result, our method achieves state-of-the-art performance on ETH3D and Tanks and Temples while preserving low memory consumption. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Adaptive_Patch_Deformation_for_Textureless-Resilient_Multi-View_Stereo_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Adaptive_Patch_Deformation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Adaptive_Patch_Deformation_for_Textureless-Resilient_Multi-View_Stereo_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Adaptive_Patch_Deformation_for_Textureless-Resilient_Multi-View_Stereo_CVPR_2023_paper.html | CVPR 2023 | null |
Detection of Out-of-Distribution Samples Using Binary Neuron Activation Patterns | Bartłomiej Olber, Krystian Radlak, Adam Popowicz, Michal Szczepankiewicz, Krystian Chachuła | Deep neural networks (DNN) have outstanding performance in various applications. Despite numerous efforts of the research community, out-of-distribution (OOD) samples remain a significant limitation of DNN classifiers. The ability to identify previously unseen inputs as novel is crucial in safety-critical applications such as self-driving cars, unmanned aerial vehicles, and robots. Existing approaches to detect OOD samples treat a DNN as a black box and evaluate the confidence score of the output predictions. Unfortunately, this method frequently fails, because DNNs are not trained to reduce their confidence for OOD inputs. In this work, we introduce a novel method for OOD detection. Our method is motivated by theoretical analysis of neuron activation patterns (NAP) in ReLU-based architectures. The proposed method does not introduce a high computational overhead due to the binary representation of the activation patterns extracted from convolutional layers. The extensive empirical evaluation proves its high performance on various DNN architectures and seven image datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Olber_Detection_of_Out-of-Distribution_Samples_Using_Binary_Neuron_Activation_Patterns_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Olber_Detection_of_Out-of-Distribution_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.14268 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Olber_Detection_of_Out-of-Distribution_Samples_Using_Binary_Neuron_Activation_Patterns_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Olber_Detection_of_Out-of-Distribution_Samples_Using_Binary_Neuron_Activation_Patterns_CVPR_2023_paper.html | CVPR 2023 | null |
SeaThru-NeRF: Neural Radiance Fields in Scattering Media | Deborah Levy, Amit Peleg, Naama Pearl, Dan Rosenbaum, Derya Akkaynak, Simon Korman, Tali Treibitz | Research on neural radiance fields (NeRFs) for novel view generation is exploding with new models and extensions. However, a question that remains unanswered is what happens in underwater or foggy scenes where the medium strongly influences the appearance of objects. Thus far, NeRF and its variants have ignored these cases. However, since the NeRF framework is based on volumetric rendering, it has inherent capability to account for the medium's effects, once modeled appropriately. We develop a new rendering model for NeRFs in scattering media, which is based on the SeaThru image formation model, and suggest a suitable architecture for learning both scene information and medium parameters. We demonstrate the strength of our method using simulated and real-world scenes, correctly rendering novel photorealistic views underwater. Even more excitingly, we can render clear views of these scenes, removing the medium between the camera and the scene and reconstructing the appearance and depth of far objects, which are severely occluded by the medium. Our code and unique datasets are available on the project's website. | https://openaccess.thecvf.com/content/CVPR2023/papers/Levy_SeaThru-NeRF_Neural_Radiance_Fields_in_Scattering_Media_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Levy_SeaThru-NeRF_Neural_Radiance_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Levy_SeaThru-NeRF_Neural_Radiance_Fields_in_Scattering_Media_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Levy_SeaThru-NeRF_Neural_Radiance_Fields_in_Scattering_Media_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Multi-Modal Class-Specific Tokens for Weakly Supervised Dense Object Localization | Lian Xu, Wanli Ouyang, Mohammed Bennamoun, Farid Boussaid, Dan Xu | Weakly supervised dense object localization (WSDOL) relies generally on Class Activation Mapping (CAM), which exploits the correlation between the class weights of the image classifier and the pixel-level features. Due to the limited ability to address intra-class variations, the image classifier cannot properly associate the pixel features, leading to inaccurate dense localization maps. In this paper, we propose to explicitly construct multi-modal class representations by leveraging the Contrastive Language-Image Pre-training (CLIP), to guide dense localization. More specifically, we propose a unified transformer framework to learn two-modalities of class-specific tokens, i.e., class-specific visual and textual tokens. The former captures semantics from the target visual data while the latter exploits the class-related language priors from CLIP, providing complementary information to better perceive the intra-class diversities. In addition, we propose to enrich the multi-modal class-specific tokens with sample-specific contexts comprising visual context and image-language context. This enables more adaptive class representation learning, which further facilitates dense localization. Extensive experiments show the superiority of the proposed method for WSDOL on two multi-label datasets, i.e., PASCAL VOC and MS COCO, and one single-label dataset, i.e., OpenImages. Our dense localization maps also lead to the state-of-the-art weakly supervised semantic segmentation (WSSS) results on PASCAL VOC and MS COCO. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Learning_Multi-Modal_Class-Specific_Tokens_for_Weakly_Supervised_Dense_Object_Localization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_Learning_Multi-Modal_Class-Specific_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Learning_Multi-Modal_Class-Specific_Tokens_for_Weakly_Supervised_Dense_Object_Localization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Learning_Multi-Modal_Class-Specific_Tokens_for_Weakly_Supervised_Dense_Object_Localization_CVPR_2023_paper.html | CVPR 2023 | null |
Learning To Dub Movies via Hierarchical Prosody Models | Gaoxiang Cong, Liang Li, Yuankai Qi, Zheng-Jun Zha, Qi Wu, Wenyu Wang, Bin Jiang, Ming-Hsuan Yang, Qingming Huang | Given a piece of text, a video clip and a reference audio, the movie dubbing (also known as visual voice clone, V2C) task aims to generate speeches that match the speaker's emotion presented in the video using the desired speaker voice as reference. V2C is more challenging than conventional text-to-speech tasks as it additionally requires the generated speech to exactly match the varying emotions and speaking speed presented in the video. Unlike previous works, we propose a novel movie dubbing architecture to tackle these problems via hierarchical prosody modeling, which bridges the visual information to corresponding speech prosody from three aspects: lip, face, and scene. Specifically, we align lip movement to the speech duration, and convey facial expression to speech energy and pitch via attention mechanism based on valence and arousal representations inspired by the psychology findings. Moreover, we design an emotion booster to capture the atmosphere from global video scenes. All these embeddings are used together to generate mel-spectrogram, which is then converted into speech waves by an existing vocoder. Extensive experimental results on the V2C and Chem benchmark datasets demonstrate the favourable performance of the proposed method. The code and trained models will be made available at https://github.com/GalaxyCong/HPMDubbing. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cong_Learning_To_Dub_Movies_via_Hierarchical_Prosody_Models_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2212.04054 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cong_Learning_To_Dub_Movies_via_Hierarchical_Prosody_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cong_Learning_To_Dub_Movies_via_Hierarchical_Prosody_Models_CVPR_2023_paper.html | CVPR 2023 | null |
DiffusionRig: Learning Personalized Priors for Facial Appearance Editing | Zheng Ding, Xuaner Zhang, Zhihao Xia, Lars Jebe, Zhuowen Tu, Xiuming Zhang | We address the problem of learning person-specific facial priors from a small number (e.g., 20) of portrait photos of the same person. This enables us to edit this specific person's facial appearance, such as expression and lighting, while preserving their identity and high-frequency facial details. Key to our approach, which we dub DiffusionRig, is a diffusion model conditioned on, or "rigged by," crude 3D face models estimated from single in-the-wild images by an off-the-shelf estimator. On a high level, DiffusionRig learns to map simplistic renderings of 3D face models to realistic photos of a given person. Specifically, DiffusionRig is trained in two stages: It first learns generic facial priors from a large-scale face dataset and then person-specific priors from a small portrait photo collection of the person of interest. By learning the CGI-to-photo mapping with such personalized priors, DiffusionRig can "rig" the lighting, facial expression, head pose, etc. of a portrait photo, conditioned only on coarse 3D models while preserving this person's identity and other high-frequency characteristics. Qualitative and quantitative experiments show that DiffusionRig outperforms existing approaches in both identity preservation and photorealism. Please see the project website: https://diffusionrig.github.io for the supplemental material, video, code, and data. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ding_DiffusionRig_Learning_Personalized_Priors_for_Facial_Appearance_Editing_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ding_DiffusionRig_Learning_Personalized_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.06711 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ding_DiffusionRig_Learning_Personalized_Priors_for_Facial_Appearance_Editing_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ding_DiffusionRig_Learning_Personalized_Priors_for_Facial_Appearance_Editing_CVPR_2023_paper.html | CVPR 2023 | null |
Delving StyleGAN Inversion for Image Editing: A Foundation Latent Space Viewpoint | Hongyu Liu, Yibing Song, Qifeng Chen | GAN inversion and editing via StyleGAN maps an input image into the embedding spaces (W, W^+, and F) to simultaneously maintain image fidelity and meaningful manipulation. From latent space W to extended latent space W^+ to feature space F in StyleGAN, the editability of GAN inversion decreases while its reconstruction quality increases. Recent GAN inversion methods typically explore W^+ and F rather than W to improve reconstruction fidelity while maintaining editability. As W^+ and F are derived from W that is essentially the foundation latent space of StyleGAN, these GAN inversion methods focusing on W^+ and F spaces could be improved by stepping back to W. In this work, we propose to first obtain the proper latent code in foundation latent space W. We introduce contrastive learning to align W and the image space for proper latent code discovery. Then, we leverage a cross-attention encoder to transform the obtained latent code in W into W^+ and F, accordingly. Our experiments show that our exploration of the foundation latent space W improves the representation ability of latent codes in W^+ and features in F, which yields state-of-the-art reconstruction fidelity and editability results on the standard benchmarks. Project page: https://kumapowerliu.github.io/CLCAE. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Delving_StyleGAN_Inversion_for_Image_Editing_A_Foundation_Latent_Space_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Delving_StyleGAN_Inversion_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.11448 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Delving_StyleGAN_Inversion_for_Image_Editing_A_Foundation_Latent_Space_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Delving_StyleGAN_Inversion_for_Image_Editing_A_Foundation_Latent_Space_CVPR_2023_paper.html | CVPR 2023 | null |
MixMAE: Mixed and Masked Autoencoder for Efficient Pretraining of Hierarchical Vision Transformers | Jihao Liu, Xin Huang, Jinliang Zheng, Yu Liu, Hongsheng Li | In this paper, we propose Mixed and Masked AutoEncoder (MixMAE), a simple but efficient pretraining method that is applicable to various hierarchical Vision Transformers. Existing masked image modeling (MIM) methods for hierarchical Vision Transformers replace a random subset of input tokens with a special [MASK] symbol and aim at reconstructing original image tokens from the corrupted image. However, we find that using the [MASK] symbol greatly slows down the training and causes pretraining-finetuning inconsistency, due to the large masking ratio (e.g., 60% in SimMIM). On the other hand, MAE does not introduce [MASK] tokens at its encoder at all but is not applicable for hierarchical Vision Transformers. To solve the issue and accelerate the pretraining of hierarchical models, we replace the masked tokens of one image with visible tokens of another image, i.e., creating a mixed image. We then conduct dual reconstruction to reconstruct the two original images from the mixed input, which significantly improves efficiency. While MixMAE can be applied to various hierarchical Transformers, this paper explores using Swin Transformer with a large window size and scales up to huge model size (to reach 600M parameters). Empirical results demonstrate that MixMAE can learn high-quality visual representations efficiently. Notably, MixMAE with Swin-B/W14 achieves 85.1% top-1 accuracy on ImageNet-1K by pretraining for 600 epochs. Besides, its transfer performances on the other 6 datasets show that MixMAE has better FLOPs / performance tradeoff than previous popular MIM methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_MixMAE_Mixed_and_Masked_Autoencoder_for_Efficient_Pretraining_of_Hierarchical_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_MixMAE_Mixed_and_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2205.13137 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_MixMAE_Mixed_and_Masked_Autoencoder_for_Efficient_Pretraining_of_Hierarchical_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_MixMAE_Mixed_and_Masked_Autoencoder_for_Efficient_Pretraining_of_Hierarchical_CVPR_2023_paper.html | CVPR 2023 | null |
Human Pose Estimation in Extremely Low-Light Conditions | Sohyun Lee, Jaesung Rim, Boseung Jeong, Geonu Kim, Byungju Woo, Haechan Lee, Sunghyun Cho, Suha Kwak | We study human pose estimation in extremely low-light images. This task is challenging due to the difficulty of collecting real low-light images with accurate labels, and severely corrupted inputs that degrade prediction quality significantly. To address the first issue, we develop a dedicated camera system and build a new dataset of real low-light images with accurate pose labels. Thanks to our camera system, each low-light image in our dataset is coupled with an aligned well-lit image, which enables accurate pose labeling and is used as privileged information during training. We also propose a new model and a new training strategy that fully exploit the privileged information to learn representation insensitive to lighting conditions. Our method demonstrates outstanding performance on real extremely low-light images, and extensive analyses validate that both of our model and dataset contribute to the success. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lee_Human_Pose_Estimation_in_Extremely_Low-Light_Conditions_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lee_Human_Pose_Estimation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15410 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_Human_Pose_Estimation_in_Extremely_Low-Light_Conditions_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_Human_Pose_Estimation_in_Extremely_Low-Light_Conditions_CVPR_2023_paper.html | CVPR 2023 | null |
EventNeRF: Neural Radiance Fields From a Single Colour Event Camera | Viktor Rudnev, Mohamed Elgharib, Christian Theobalt, Vladislav Golyanik | Asynchronously operating event cameras find many applications due to their high dynamic range, vanishingly low motion blur, low latency and low data bandwidth. The field saw remarkable progress during the last few years, and existing event-based 3D reconstruction approaches recover sparse point clouds of the scene. However, such sparsity is a limiting factor in many cases, especially in computer vision and graphics, that has not been addressed satisfactorily so far. Accordingly, this paper proposes the first approach for 3D-consistent, dense and photorealistic novel view synthesis using just a single colour event stream as input. At its core is a neural radiance field trained entirely in a self-supervised manner from events while preserving the original resolution of the colour event channels. Next, our ray sampling strategy is tailored to events and allows for data-efficient training. At test, our method produces results in the RGB space at unprecedented quality. We evaluate our method qualitatively and numerically on several challenging synthetic and real scenes and show that it produces significantly denser and more visually appealing renderings than the existing methods. We also demonstrate robustness in challenging scenarios with fast motion and under low lighting conditions. We release the newly recorded dataset and our source code to facilitate the research field, see https://4dqv.mpi-inf.mpg.de/EventNeRF. | https://openaccess.thecvf.com/content/CVPR2023/papers/Rudnev_EventNeRF_Neural_Radiance_Fields_From_a_Single_Colour_Event_Camera_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Rudnev_EventNeRF_Neural_Radiance_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2206.11896 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Rudnev_EventNeRF_Neural_Radiance_Fields_From_a_Single_Colour_Event_Camera_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Rudnev_EventNeRF_Neural_Radiance_Fields_From_a_Single_Colour_Event_Camera_CVPR_2023_paper.html | CVPR 2023 | null |
Neighborhood Attention Transformer | Ali Hassani, Steven Walton, Jiachen Li, Shen Li, Humphrey Shi | We present Neighborhood Attention (NA), the first efficient and scalable sliding window attention mechanism for vision. NA is a pixel-wise operation, localizing self attention (SA) to the nearest neighboring pixels, and therefore enjoys a linear time and space complexity compared to the quadratic complexity of SA. The sliding window pattern allows NA's receptive field to grow without needing extra pixel shifts, and preserves translational equivariance, unlike Swin Transformer's Window Self Attention (WSA). We develop NATTEN (Neighborhood Attention Extension), a Python package with efficient C++ and CUDA kernels, which allows NA to run up to 40% faster than Swin's WSA while using up to 25% less memory. We further present Neighborhood Attention Transformer (NAT), a new hierarchical transformer design based on NA that boosts image classification and downstream vision performance. Experimental results on NAT are competitive; NAT-Tiny reaches 83.2% top-1 accuracy on ImageNet, 51.4% mAP on MS-COCO and 48.4% mIoU on ADE20K, which is 1.9% ImageNet accuracy, 1.0% COCO mAP, and 2.6% ADE20K mIoU improvement over a Swin model with similar size. To support more research based on sliding window attention, we open source our project and release our checkpoints. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hassani_Neighborhood_Attention_Transformer_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hassani_Neighborhood_Attention_Transformer_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2204.07143 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hassani_Neighborhood_Attention_Transformer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hassani_Neighborhood_Attention_Transformer_CVPR_2023_paper.html | CVPR 2023 | null |
Enlarging Instance-Specific and Class-Specific Information for Open-Set Action Recognition | null | null | null | null | null | null | https://openaccess.thecvf.com/content/CVPR2023/html/Cen_Enlarging_Instance-Specific_and_Class-Specific_Information_for_Open-Set_Action_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cen_Enlarging_Instance-Specific_and_Class-Specific_Information_for_Open-Set_Action_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
Decoupled Semantic Prototypes Enable Learning From Diverse Annotation Types for Semi-Weakly Segmentation in Expert-Driven Domains | Simon Reiß, Constantin Seibold, Alexander Freytag, Erik Rodner, Rainer Stiefelhagen | A vast amount of images and pixel-wise annotations allowed our community to build scalable segmentation solutions for natural domains. However, the transfer to expert-driven domains like microscopy applications or medical healthcare remains difficult as domain experts are a critical factor due to their limited availability for providing pixel-wise annotations. To enable affordable segmentation solutions for such domains, we need training strategies which can simultaneously handle diverse annotation types and are not bound to costly pixel-wise annotations. In this work, we analyze existing training algorithms towards their flexibility for different annotation types and scalability to small annotation regimes. We conduct an extensive evaluation in the challenging domain of organelle segmentation and find that existing semi- and semi-weakly supervised training algorithms are not able to fully exploit diverse annotation types. Driven by our findings, we introduce Decoupled Semantic Prototypes (DSP) as a training method for semantic segmentation which enables learning from annotation types as diverse as image-level-, point-, bounding box-, and pixel-wise annotations and which leads to remarkable accuracy gains over existing solutions for semi-weakly segmentation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Reiss_Decoupled_Semantic_Prototypes_Enable_Learning_From_Diverse_Annotation_Types_for_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Reiss_Decoupled_Semantic_Prototypes_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Reiss_Decoupled_Semantic_Prototypes_Enable_Learning_From_Diverse_Annotation_Types_for_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Reiss_Decoupled_Semantic_Prototypes_Enable_Learning_From_Diverse_Annotation_Types_for_CVPR_2023_paper.html | CVPR 2023 | null |
Progressive Spatio-Temporal Alignment for Efficient Event-Based Motion Estimation | Xueyan Huang, Yueyi Zhang, Zhiwei Xiong | In this paper, we propose an efficient event-based motion estimation framework for various motion models. Different from previous works, we design a progressive event-to-map alignment scheme and utilize the spatio-temporal correlations to align events. In detail, we progressively align sampled events in an event batch to the time-surface map and obtain the updated motion model by minimizing a novel time-surface loss. In addition, a dynamic batch size strategy is applied to adaptively adjust the batch size so that all events in the batch are consistent with the current motion model. Our framework has three advantages: a) the progressive scheme refines motion parameters iteratively, achieving accurate motion estimation; b) within one iteration, only a small portion of events are involved in optimization, which greatly reduces the total runtime; c) the dynamic batch size strategy ensures that the constant velocity assumption always holds. We conduct comprehensive experiments to evaluate our framework on challenging high-speed scenes with three motion models: rotational, homography, and 6-DOF models. Experimental results demonstrate that our framework achieves state-of-the-art estimation accuracy and efficiency. | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Progressive_Spatio-Temporal_Alignment_for_Efficient_Event-Based_Motion_Estimation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_Progressive_Spatio-Temporal_Alignment_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Progressive_Spatio-Temporal_Alignment_for_Efficient_Event-Based_Motion_Estimation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Progressive_Spatio-Temporal_Alignment_for_Efficient_Event-Based_Motion_Estimation_CVPR_2023_paper.html | CVPR 2023 | null |
Trap Attention: Monocular Depth Estimation With Manual Traps | Chao Ning, Hongping Gan | Predicting a high quality depth map from a single image is a challenging task, because it exists infinite possibility to project a 2D scene to the corresponding 3D scene. Recently, some studies introduced multi-head attention (MHA) modules to perform long-range interaction, which have shown significant progress in regressing the depth maps.The main functions of MHA can be loosely summarized to capture long-distance information and report the attention map by the relationship between pixels. However, due to the quadratic complexity of MHA, these methods can not leverage MHA to compute depth features in high resolution with an appropriate computational complexity. In this paper, we exploit a depth-wise convolution to obtain long-range information, and propose a novel trap attention, which sets some traps on the extended space for each pixel, and forms the attention mechanism by the feature retention ratio of convolution window, resulting in that the quadratic computational complexity can be converted to linear form. Then we build an encoder-decoder trap depth estimation network, which introduces a vision transformer as the encoder, and uses the trap attention to estimate the depth from single image in the decoder. Extensive experimental results demonstrate that our proposed network can outperform the state-of-the-art methods in monocular depth estimation on datasets NYU Depth-v2 and KITTI, with significantly reduced number of parameters. Code is available at: https://github.com/ICSResearch/TrapAttention. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ning_Trap_Attention_Monocular_Depth_Estimation_With_Manual_Traps_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ning_Trap_Attention_Monocular_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ning_Trap_Attention_Monocular_Depth_Estimation_With_Manual_Traps_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ning_Trap_Attention_Monocular_Depth_Estimation_With_Manual_Traps_CVPR_2023_paper.html | CVPR 2023 | null |
Iterative Next Boundary Detection for Instance Segmentation of Tree Rings in Microscopy Images of Shrub Cross Sections | Alexander Gillert, Giulia Resente, Alba Anadon-Rosell, Martin Wilmking, Uwe Freiherr von Lukas | We address the problem of detecting tree rings in microscopy images of shrub cross sections. This can be regarded as a special case of the instance segmentation task with several unique challenges such as the concentric circular ring shape of the objects and high precision requirements that result in inadequate performance of existing methods. We propose a new iterative method which we term Iterative Next Boundary Detection (INBD). It intuitively models the natural growth direction, starting from the center of the shrub cross section and detecting the next ring boundary in each iteration step. In our experiments, INBD shows superior performance to generic instance segmentation methods and is the only one with a built-in notion of chronological order. Our dataset and source code are available at http://github.com/alexander-g/INBD. | https://openaccess.thecvf.com/content/CVPR2023/papers/Gillert_Iterative_Next_Boundary_Detection_for_Instance_Segmentation_of_Tree_Rings_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gillert_Iterative_Next_Boundary_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.03022 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gillert_Iterative_Next_Boundary_Detection_for_Instance_Segmentation_of_Tree_Rings_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gillert_Iterative_Next_Boundary_Detection_for_Instance_Segmentation_of_Tree_Rings_CVPR_2023_paper.html | CVPR 2023 | null |
Learning and Aggregating Lane Graphs for Urban Automated Driving | Martin Büchner, Jannik Zürn, Ion-George Todoran, Abhinav Valada, Wolfram Burgard | Lane graph estimation is an essential and highly challenging task in automated driving and HD map learning. Existing methods using either onboard or aerial imagery struggle with complex lane topologies, out-of-distribution scenarios, or significant occlusions in the image space. Moreover, merging overlapping lane graphs to obtain consistent largescale graphs remains difficult. To overcome these challenges, we propose a novel bottom-up approach to lane graph estimation from aerial imagery that aggregates multiple overlapping graphs into a single consistent graph. Due to its modular design, our method allows us to address two complementary tasks: predicting ego-respective successor lane graphs from arbitrary vehicle positions using a graph neural network and aggregating these predictions into a consistent global lane graph. Extensive experiments on a large-scale lane graph dataset demonstrate that our approach yields highly accurate lane graphs, even in regions with severe occlusions. The presented approach to graph aggregation proves to eliminate inconsistent predictions while increasing the overall graph quality. We make our large-scale urban lane graph dataset and code publicly available at http://urbanlanegraph.cs.uni-freiburg.de. | https://openaccess.thecvf.com/content/CVPR2023/papers/Buchner_Learning_and_Aggregating_Lane_Graphs_for_Urban_Automated_Driving_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Buchner_Learning_and_Aggregating_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Buchner_Learning_and_Aggregating_Lane_Graphs_for_Urban_Automated_Driving_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Buchner_Learning_and_Aggregating_Lane_Graphs_for_Urban_Automated_Driving_CVPR_2023_paper.html | CVPR 2023 | null |
Universal Instance Perception As Object Discovery and Retrieval | Bin Yan, Yi Jiang, Jiannan Wu, Dong Wang, Ping Luo, Zehuan Yuan, Huchuan Lu | All instance perception tasks aim at finding certain objects specified by some queries such as category names, language expressions, and target annotations, but this complete field has been split into multiple independent subtasks. In this work, we present a universal instance perception model of the next generation, termed UNINEXT. UNINEXT reformulates diverse instance perception tasks into a unified object discovery and retrieval paradigm and can flexibly perceive different types of objects by simply changing the input prompts. This unified formulation brings the following benefits: (1) enormous data from different tasks and label vocabularies can be exploited for jointly training general instance-level representations, which is especially beneficial for tasks lacking in training data. (2) the unified model is parameter-efficient and can save redundant computation when handling multiple tasks simultaneously. UNINEXT shows superior performance on 20 challenging benchmarks from 10 instance-level tasks including classical image-level tasks (object detection and instance segmentation), vision-and-language tasks (referring expression comprehension and segmentation), and six video-level object tracking tasks. Code is available at https://github.com/MasterBin-IIAU/UNINEXT. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yan_Universal_Instance_Perception_As_Object_Discovery_and_Retrieval_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yan_Universal_Instance_Perception_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.06674 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yan_Universal_Instance_Perception_As_Object_Discovery_and_Retrieval_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yan_Universal_Instance_Perception_As_Object_Discovery_and_Retrieval_CVPR_2023_paper.html | CVPR 2023 | null |
GlassesGAN: Eyewear Personalization Using Synthetic Appearance Discovery and Targeted Subspace Modeling | Richard Plesh, Peter Peer, Vitomir Struc | We present GlassesGAN, a novel image editing framework for custom design of glasses, that sets a new standard in terms of output-image quality, edit realism, and continuous multi-style edit capability. To facilitate the editing process with GlassesGAN, we propose a Targeted Subspace Modelling (TSM) procedure that, based on a novel mechanism for (synthetic) appearance discovery in the latent space of a pre-trained GAN generator, constructs an eyeglasses-specific (latent) subspace that the editing framework can utilize. Additionally, we also introduce an appearance-constrained subspace initialization (SI) technique that centers the latent representation of the given input image in the well-defined part of the constructed subspace to improve the reliability of the learned edits. We test GlassesGAN on two (diverse) high-resolution datasets (CelebA-HQ and SiblingsDB-HQf) and compare it to three state-of-the-art baselines, i.e., InterfaceGAN, GANSpace, and MaskGAN. The reported results show that GlassesGAN convincingly outperforms all competing techniques, while offering functionality (e.g., fine-grained multi-style editing) not available with any of the competitors. The source code for GlassesGAN is made publicly available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Plesh_GlassesGAN_Eyewear_Personalization_Using_Synthetic_Appearance_Discovery_and_Targeted_Subspace_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Plesh_GlassesGAN_Eyewear_Personalization_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2210.14145 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Plesh_GlassesGAN_Eyewear_Personalization_Using_Synthetic_Appearance_Discovery_and_Targeted_Subspace_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Plesh_GlassesGAN_Eyewear_Personalization_Using_Synthetic_Appearance_Discovery_and_Targeted_Subspace_CVPR_2023_paper.html | CVPR 2023 | null |
Representing Volumetric Videos As Dynamic MLP Maps | Sida Peng, Yunzhi Yan, Qing Shuai, Hujun Bao, Xiaowei Zhou | This paper introduces a novel representation of volumetric videos for real-time view synthesis of dynamic scenes. Recent advances in neural scene representations demonstrate their remarkable capability to model and render complex static scenes, but extending them to represent dynamic scenes is not straightforward due to their slow rendering speed or high storage cost. To solve this problem, our key idea is to represent the radiance field of each frame as a set of shallow MLP networks whose parameters are stored in 2D grids, called MLP maps, and dynamically predicted by a 2D CNN decoder shared by all frames. Representing 3D scenes with shallow MLPs significantly improves the rendering speed, while dynamically predicting MLP parameters with a shared 2D CNN instead of explicitly storing them leads to low storage cost. Experiments show that the proposed approach achieves state-of-the-art rendering quality on the NHR and ZJU-MoCap datasets, while being efficient for real-time rendering with a speed of 41.7 fps for 512 x 512 images on an RTX 3090 GPU. The code is available at https://zju3dv.github.io/mlp_maps/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Peng_Representing_Volumetric_Videos_As_Dynamic_MLP_Maps_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2304.06717 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Peng_Representing_Volumetric_Videos_As_Dynamic_MLP_Maps_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Peng_Representing_Volumetric_Videos_As_Dynamic_MLP_Maps_CVPR_2023_paper.html | CVPR 2023 | null |
Deep Hashing With Minimal-Distance-Separated Hash Centers | Liangdao Wang, Yan Pan, Cong Liu, Hanjiang Lai, Jian Yin, Ye Liu | Deep hashing is an appealing approach for large-scale image retrieval. Most existing supervised deep hashing methods learn hash functions using pairwise or triple image similarities in randomly sampled mini-batches. They suffer from low training efficiency, insufficient coverage of data distribution, and pair imbalance problems. Recently, central similarity quantization (CSQ) attacks the above problems by using "hash centers" as a global similarity metric, which encourages the hash codes of similar images to approach their common hash center and distance themselves from other hash centers. Although achieving SOTA retrieval performance, CSQ falls short of a worst-case guarantee on the minimal distance between its constructed hash centers, i.e. the hash centers can be arbitrarily close. This paper presents an optimization method that finds hash centers with a constraint on the minimal distance between any pair of hash centers, which is non-trivial due to the non-convex nature of the problem. More importantly, we adopt the Gilbert-Varshamov bound from coding theory, which helps us to obtain a large minimal distance while ensuring the empirical feasibility of our optimization approach. With these clearly-separated hash centers, each is assigned to one image class, we propose several effective loss functions to train deep hashing networks. Extensive experiments on three datasets for image retrieval demonstrate that the proposed method achieves superior retrieval performance over the state-of-the-art deep hashing methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Deep_Hashing_With_Minimal-Distance-Separated_Hash_Centers_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Deep_Hashing_With_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Deep_Hashing_With_Minimal-Distance-Separated_Hash_Centers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Deep_Hashing_With_Minimal-Distance-Separated_Hash_Centers_CVPR_2023_paper.html | CVPR 2023 | null |
Video-Text As Game Players: Hierarchical Banzhaf Interaction for Cross-Modal Representation Learning | Peng Jin, Jinfa Huang, Pengfei Xiong, Shangxuan Tian, Chang Liu, Xiangyang Ji, Li Yuan, Jie Chen | Contrastive learning-based video-language representation learning approaches, e.g., CLIP, have achieved outstanding performance, which pursue semantic interaction upon pre-defined video-text pairs. To clarify this coarse-grained global interaction and move a step further, we have to encounter challenging shell-breaking interactions for fine-grained cross-modal learning. In this paper, we creatively model video-text as game players with multivariate cooperative game theory to wisely handle the uncertainty during fine-grained semantic interaction with diverse granularity, flexible combination, and vague intensity. Concretely, we propose Hierarchical Banzhaf Interaction (HBI) to value possible correspondence between video frames and text words for sensitive and explainable cross-modal contrast. To efficiently realize the cooperative game of multiple video frames and multiple text words, the proposed method clusters the original video frames (text words) and computes the Banzhaf Interaction between the merged tokens. By stacking token merge modules, we achieve cooperative games at different semantic levels. Extensive experiments on commonly used text-video retrieval and video-question answering benchmarks with superior performances justify the efficacy of our HBI. More encouragingly, it can also serve as a visualization tool to promote the understanding of cross-modal interaction, which may have a far-reaching impact on the community. Project page is available at https://jpthu17.github.io/HBI/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jin_Video-Text_As_Game_Players_Hierarchical_Banzhaf_Interaction_for_Cross-Modal_Representation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jin_Video-Text_As_Game_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14369 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jin_Video-Text_As_Game_Players_Hierarchical_Banzhaf_Interaction_for_Cross-Modal_Representation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jin_Video-Text_As_Game_Players_Hierarchical_Banzhaf_Interaction_for_Cross-Modal_Representation_CVPR_2023_paper.html | CVPR 2023 | null |
VL-SAT: Visual-Linguistic Semantics Assisted Training for 3D Semantic Scene Graph Prediction in Point Cloud | Ziqin Wang, Bowen Cheng, Lichen Zhao, Dong Xu, Yang Tang, Lu Sheng | The task of 3D semantic scene graph (3DSSG) prediction in the point cloud is challenging since (1) the 3D point cloud only captures geometric structures with limited semantics compared to 2D images, and (2) long-tailed relation distribution inherently hinders the learning of unbiased prediction. Since 2D images provide rich semantics and scene graphs are in nature coped with languages, in this study, we propose Visual-Linguistic Semantics Assisted Training (VL-SAT) scheme that can significantly empower 3DSSG prediction models with discrimination about long-tailed and ambiguous semantic relations. The key idea is to train a powerful multi-modal oracle model to assist the 3D model. This oracle learns reliable structural representations based on semantics from vision, language, and 3D geometry, and its benefits can be heterogeneously passed to the 3D model during the training stage. By effectively utilizing visual-linguistic semantics in training, our VL-SAT can significantly boost common 3DSSG prediction models, such as SGFN and SGGpoint, only with 3D inputs in the inference stage, especially when dealing with tail relation triplets. Comprehensive evaluations and ablation studies on the 3DSSG dataset have validated the effectiveness of the proposed scheme. Code is available at https://github.com/wz7in/CVPR2023-VLSAT. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_VL-SAT_Visual-Linguistic_Semantics_Assisted_Training_for_3D_Semantic_Scene_Graph_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_VL-SAT_Visual-Linguistic_Semantics_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_VL-SAT_Visual-Linguistic_Semantics_Assisted_Training_for_3D_Semantic_Scene_Graph_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_VL-SAT_Visual-Linguistic_Semantics_Assisted_Training_for_3D_Semantic_Scene_Graph_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Emotion Representations From Verbal and Nonverbal Communication | Sitao Zhang, Yimu Pan, James Z. Wang | Emotion understanding is an essential but highly challenging component of artificial general intelligence. The absence of extensive annotated datasets has significantly impeded advancements in this field. We present EmotionCLIP, the first pre-training paradigm to extract visual emotion representations from verbal and nonverbal communication using only uncurated data. Compared to numerical labels or descriptions used in previous methods, communication naturally contains emotion information. Furthermore, acquiring emotion representations from communication is more congruent with the human learning process. We guide EmotionCLIP to attend to nonverbal emotion cues through subject-aware context encoding and verbal emotion cues using sentiment-guided contrastive learning. Extensive experiments validate the effectiveness and transferability of EmotionCLIP. Using merely linear-probe evaluation protocol, EmotionCLIP outperforms the state-of-the-art supervised visual emotion recognition methods and rivals many multimodal approaches across various benchmarks. We anticipate that the advent of EmotionCLIP will address the prevailing issue of data scarcity in emotion understanding, thereby fostering progress in related domains. The code and pre-trained models are available at https://github.com/Xeaver/EmotionCLIP. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Learning_Emotion_Representations_From_Verbal_and_Nonverbal_Communication_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Learning_Emotion_Representations_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Learning_Emotion_Representations_From_Verbal_and_Nonverbal_Communication_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Learning_Emotion_Representations_From_Verbal_and_Nonverbal_Communication_CVPR_2023_paper.html | CVPR 2023 | null |
Transferable Adversarial Attacks on Vision Transformers With Token Gradient Regularization | Jianping Zhang, Yizhan Huang, Weibin Wu, Michael R. Lyu | Vision transformers (ViTs) have been successfully deployed in a variety of computer vision tasks, but they are still vulnerable to adversarial samples. Transfer-based attacks use a local model to generate adversarial samples and directly transfer them to attack a target black-box model. The high efficiency of transfer-based attacks makes it a severe security threat to ViT-based applications. Therefore, it is vital to design effective transfer-based attacks to identify the deficiencies of ViTs beforehand in security-sensitive scenarios. Existing efforts generally focus on regularizing the input gradients to stabilize the updated direction of adversarial samples. However, the variance of the back-propagated gradients in intermediate blocks of ViTs may still be large, which may make the generated adversarial samples focus on some model-specific features and get stuck in poor local optima. To overcome the shortcomings of existing approaches, we propose the Token Gradient Regularization (TGR) method. According to the structural characteristics of ViTs, TGR reduces the variance of the back-propagated gradient in each internal block of ViTs in a token-wise manner and utilizes the regularized gradient to generate adversarial samples. Extensive experiments on attacking both ViTs and CNNs confirm the superiority of our approach. Notably, compared to the state-of-the-art transfer-based attacks, our TGR offers a performance improvement of 8.8 % on average. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Transferable_Adversarial_Attacks_on_Vision_Transformers_With_Token_Gradient_Regularization_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.15754 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Transferable_Adversarial_Attacks_on_Vision_Transformers_With_Token_Gradient_Regularization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Transferable_Adversarial_Attacks_on_Vision_Transformers_With_Token_Gradient_Regularization_CVPR_2023_paper.html | CVPR 2023 | null |
MCF: Mutual Correction Framework for Semi-Supervised Medical Image Segmentation | Yongchao Wang, Bin Xiao, Xiuli Bi, Weisheng Li, Xinbo Gao | Semi-supervised learning is a promising method for medical image segmentation under limited annotation. However, the model cognitive bias impairs the segmentation performance, especially for edge regions. Furthermore, current mainstream semi-supervised medical image segmentation (SSMIS) methods lack designs to handle model bias. The neural network has a strong learning ability, but the cognitive bias will gradually deepen during the training, and it is difficult to correct itself. We propose a novel mutual correction framework (MCF) to explore network bias correction and improve the performance of SSMIS. Inspired by the plain contrast idea, MCF introduces two different subnets to explore and utilize the discrepancies between subnets to correct cognitive bias of the model. More concretely, a contrastive difference review (CDR) module is proposed to find out inconsistent prediction regions and perform a review training. Additionally, a dynamic competitive pseudo-label generation (DCPLG) module is proposed to evaluate the performance of subnets in real-time, dynamically selecting more reliable pseudo-labels. Experimental results on two medical image databases with different modalities (CT and MRI) show that our method achieves superior performance compared to several state-of-the-art methods. The code will be available at https://github.com/WYC-321/MCF. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_MCF_Mutual_Correction_Framework_for_Semi-Supervised_Medical_Image_Segmentation_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_MCF_Mutual_Correction_Framework_for_Semi-Supervised_Medical_Image_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_MCF_Mutual_Correction_Framework_for_Semi-Supervised_Medical_Image_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
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