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NVLM: Open Frontier-Class Multimodal LLMs
Paper • 2409.11402 • Published • 72 -
BRAVE: Broadening the visual encoding of vision-language models
Paper • 2404.07204 • Published • 18 -
Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
Paper • 2403.18814 • Published • 45 -
Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Multimodal Models
Paper • 2409.17146 • Published • 106
Collections
Discover the best community collections!
Collections including paper arxiv:2408.12637
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iVideoGPT: Interactive VideoGPTs are Scalable World Models
Paper • 2405.15223 • Published • 12 -
Meteor: Mamba-based Traversal of Rationale for Large Language and Vision Models
Paper • 2405.15574 • Published • 53 -
An Introduction to Vision-Language Modeling
Paper • 2405.17247 • Published • 87 -
Matryoshka Multimodal Models
Paper • 2405.17430 • Published • 31
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Writing in the Margins: Better Inference Pattern for Long Context Retrieval
Paper • 2408.14906 • Published • 138 -
Training Language Models to Self-Correct via Reinforcement Learning
Paper • 2409.12917 • Published • 136 -
Towards a Unified View of Preference Learning for Large Language Models: A Survey
Paper • 2409.02795 • Published • 71 -
Attention Heads of Large Language Models: A Survey
Paper • 2409.03752 • Published • 89
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Building and better understanding vision-language models: insights and future directions
Paper • 2408.12637 • Published • 124 -
Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model
Paper • 2408.11039 • Published • 58 -
Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming
Paper • 2408.16725 • Published • 52 -
Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders
Paper • 2408.15998 • Published • 84
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What matters when building vision-language models?
Paper • 2405.02246 • Published • 101 -
MUMU: Bootstrapping Multimodal Image Generation from Text-to-Image Data
Paper • 2406.18790 • Published • 33 -
Building and better understanding vision-language models: insights and future directions
Paper • 2408.12637 • Published • 124 -
Show-o: One Single Transformer to Unify Multimodal Understanding and Generation
Paper • 2408.12528 • Published • 51
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Law of Vision Representation in MLLMs
Paper • 2408.16357 • Published • 92 -
CogVLM2: Visual Language Models for Image and Video Understanding
Paper • 2408.16500 • Published • 56 -
Learning to Move Like Professional Counter-Strike Players
Paper • 2408.13934 • Published • 23 -
Building and better understanding vision-language models: insights and future directions
Paper • 2408.12637 • Published • 124