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NVLM: Open Frontier-Class Multimodal LLMs
Paper • 2409.11402 • Published • 71 -
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 • 44 -
Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Multimodal Models
Paper • 2409.17146 • Published • 99
Collections
Discover the best community collections!
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 • 126 -
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 • 85
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EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 25 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 12 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 38 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 19
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LLaVA-Interactive: An All-in-One Demo for Image Chat, Segmentation, Generation and Editing
Paper • 2311.00571 • Published • 40 -
LLaVA-Plus: Learning to Use Tools for Creating Multimodal Agents
Paper • 2311.05437 • Published • 45 -
Ziya-VL: Bilingual Large Vision-Language Model via Multi-Task Instruction Tuning
Paper • 2310.08166 • Published • 1 -
Reformulating Vision-Language Foundation Models and Datasets Towards Universal Multimodal Assistants
Paper • 2310.00653 • Published • 3
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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 • 134 -
Towards a Unified View of Preference Learning for Large Language Models: A Survey
Paper • 2409.02795 • Published • 72 -
Attention Heads of Large Language Models: A Survey
Paper • 2409.03752 • Published • 87
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Building and better understanding vision-language models: insights and future directions
Paper • 2408.12637 • Published • 116 -
Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model
Paper • 2408.11039 • Published • 56 -
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 • 83
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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 • 116 -
Show-o: One Single Transformer to Unify Multimodal Understanding and Generation
Paper • 2408.12528 • Published • 50
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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 • 21 -
Building and better understanding vision-language models: insights and future directions
Paper • 2408.12637 • Published • 116