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Collections including paper arxiv:2408.12637

EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters

Paper • 2402.04252 • Published Feb 6, 2024 • 28
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models

Paper • 2402.03749 • Published Feb 6, 2024 • 13
ScreenAI: A Vision-Language Model for UI and Infographics Understanding

Paper • 2402.04615 • Published Feb 7, 2024 • 44
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss

Paper • 2402.05008 • Published Feb 7, 2024 • 23

The Evolution of Multimodal Model Architectures

Paper • 2405.17927 • Published May 28, 2024 • 1
What matters when building vision-language models?

Paper • 2405.02246 • Published May 3, 2024 • 104
Efficient Architectures for High Resolution Vision-Language Models

Paper • 2501.02584 • Published Jan 5
Building and better understanding vision-language models: insights and future directions

Paper • 2408.12637 • Published Aug 22, 2024 • 131

Exploring the Potential of Encoder-free Architectures in 3D LMMs

Paper • 2502.09620 • Published Feb 13 • 26
The Evolution of Multimodal Model Architectures

Paper • 2405.17927 • Published May 28, 2024 • 1
What matters when building vision-language models?

Paper • 2405.02246 • Published May 3, 2024 • 104
Efficient Architectures for High Resolution Vision-Language Models

Paper • 2501.02584 • Published Jan 5

A Single Transformer for Scalable Vision-Language Modeling

Paper • 2407.06438 • Published Jul 8, 2024 • 1
Building and better understanding vision-language models: insights and future directions

Paper • 2408.12637 • Published Aug 22, 2024 • 131

Vision-Language Models

Building and better understanding vision-language models: insights and future directions

Paper • 2408.12637 • Published Aug 22, 2024 • 131

Learn: Vision Language Models

What matters when building vision-language models?

Paper • 2405.02246 • Published May 3, 2024 • 104
Building and better understanding vision-language models: insights and future directions

Paper • 2408.12637 • Published Aug 22, 2024 • 131

MIT Talk 31/10 Papers

NVLM: Open Frontier-Class Multimodal LLMs

Paper • 2409.11402 • Published Sep 17, 2024 • 75
BRAVE: Broadening the visual encoding of vision-language models

Paper • 2404.07204 • Published Apr 10, 2024 • 19
Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models

Paper • 2403.18814 • Published Mar 27, 2024 • 48
Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Multimodal Models

Paper • 2409.17146 • Published Sep 25, 2024 • 114

Writing in the Margins: Better Inference Pattern for Long Context Retrieval

Paper • 2408.14906 • Published Aug 27, 2024 • 142
Training Language Models to Self-Correct via Reinforcement Learning

Paper • 2409.12917 • Published Sep 19, 2024 • 139
Towards a Unified View of Preference Learning for Large Language Models: A Survey

Paper • 2409.02795 • Published Sep 4, 2024 • 73
Attention Heads of Large Language Models: A Survey

Paper • 2409.03752 • Published Sep 5, 2024 • 90

Building and better understanding vision-language models: insights and future directions

Paper • 2408.12637 • Published Aug 22, 2024 • 131

Multimodal LLMs

Building and better understanding vision-language models: insights and future directions

Paper • 2408.12637 • Published Aug 22, 2024 • 131
Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model

Paper • 2408.11039 • Published Aug 20, 2024 • 62
Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming

Paper • 2408.16725 • Published Aug 29, 2024 • 54
Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders

Paper • 2408.15998 • Published Aug 28, 2024 • 88

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