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What matters when building vision-language models?
Paper • 2405.02246 • Published • 98 -
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 • 109 -
Show-o: One Single Transformer to Unify Multimodal Understanding and Generation
Paper • 2408.12528 • Published • 50
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Collections including paper arxiv:2408.12637
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RLHF Workflow: From Reward Modeling to Online RLHF
Paper • 2405.07863 • Published • 67 -
Chameleon: Mixed-Modal Early-Fusion Foundation Models
Paper • 2405.09818 • Published • 125 -
Meteor: Mamba-based Traversal of Rationale for Large Language and Vision Models
Paper • 2405.15574 • Published • 52 -
An Introduction to Vision-Language Modeling
Paper • 2405.17247 • Published • 84
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Building and better understanding vision-language models: insights and future directions
Paper • 2408.12637 • Published • 109 -
Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model
Paper • 2408.11039 • Published • 54 -
Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming
Paper • 2408.16725 • Published • 49 -
Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders
Paper • 2408.15998 • Published • 81
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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 • 55 -
Learning to Move Like Professional Counter-Strike Players
Paper • 2408.13934 • Published • 21 -
Building and better understanding vision-language models: insights and future directions
Paper • 2408.12637 • Published • 109