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EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 29 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 13 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 44 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 23
Collections
Discover the best community collections!
Collections including paper arxiv:2408.12637
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The Evolution of Multimodal Model Architectures
Paper • 2405.17927 • Published • 1 -
What matters when building vision-language models?
Paper • 2405.02246 • Published • 104 -
Efficient Architectures for High Resolution Vision-Language Models
Paper • 2501.02584 • Published -
Building and better understanding vision-language models: insights and future directions
Paper • 2408.12637 • Published • 132
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A Single Transformer for Scalable Vision-Language Modeling
Paper • 2407.06438 • Published • 1 -
Building and better understanding vision-language models: insights and future directions
Paper • 2408.12637 • Published • 132 -
A Survey of Context Engineering for Large Language Models
Paper • 2507.13334 • Published • 244 -
MemOS: A Memory OS for AI System
Paper • 2507.03724 • Published • 150
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Writing in the Margins: Better Inference Pattern for Long Context Retrieval
Paper • 2408.14906 • Published • 143 -
Training Language Models to Self-Correct via Reinforcement Learning
Paper • 2409.12917 • Published • 141 -
Towards a Unified View of Preference Learning for Large Language Models: A Survey
Paper • 2409.02795 • Published • 74 -
Attention Heads of Large Language Models: A Survey
Paper • 2409.03752 • Published • 93
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Memory Augmented Language Models through Mixture of Word Experts
Paper • 2311.10768 • Published • 18 -
System 2 Attention (is something you might need too)
Paper • 2311.11829 • Published • 44 -
Fine-tuning Language Models for Factuality
Paper • 2311.08401 • Published • 30 -
Orca 2: Teaching Small Language Models How to Reason
Paper • 2311.11045 • Published • 77
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Exploring the Potential of Encoder-free Architectures in 3D LMMs
Paper • 2502.09620 • Published • 26 -
The Evolution of Multimodal Model Architectures
Paper • 2405.17927 • Published • 1 -
What matters when building vision-language models?
Paper • 2405.02246 • Published • 104 -
Efficient Architectures for High Resolution Vision-Language Models
Paper • 2501.02584 • Published
-
NVLM: Open Frontier-Class Multimodal LLMs
Paper • 2409.11402 • Published • 75 -
BRAVE: Broadening the visual encoding of vision-language models
Paper • 2404.07204 • Published • 19 -
Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
Paper • 2403.18814 • Published • 48 -
Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Multimodal Models
Paper • 2409.17146 • Published • 122
-
EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 29 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 13 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 44 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 23
-
Memory Augmented Language Models through Mixture of Word Experts
Paper • 2311.10768 • Published • 18 -
System 2 Attention (is something you might need too)
Paper • 2311.11829 • Published • 44 -
Fine-tuning Language Models for Factuality
Paper • 2311.08401 • Published • 30 -
Orca 2: Teaching Small Language Models How to Reason
Paper • 2311.11045 • Published • 77
-
The Evolution of Multimodal Model Architectures
Paper • 2405.17927 • Published • 1 -
What matters when building vision-language models?
Paper • 2405.02246 • Published • 104 -
Efficient Architectures for High Resolution Vision-Language Models
Paper • 2501.02584 • Published -
Building and better understanding vision-language models: insights and future directions
Paper • 2408.12637 • Published • 132
-
Exploring the Potential of Encoder-free Architectures in 3D LMMs
Paper • 2502.09620 • Published • 26 -
The Evolution of Multimodal Model Architectures
Paper • 2405.17927 • Published • 1 -
What matters when building vision-language models?
Paper • 2405.02246 • Published • 104 -
Efficient Architectures for High Resolution Vision-Language Models
Paper • 2501.02584 • Published
-
A Single Transformer for Scalable Vision-Language Modeling
Paper • 2407.06438 • Published • 1 -
Building and better understanding vision-language models: insights and future directions
Paper • 2408.12637 • Published • 132 -
A Survey of Context Engineering for Large Language Models
Paper • 2507.13334 • Published • 244 -
MemOS: A Memory OS for AI System
Paper • 2507.03724 • Published • 150
-
NVLM: Open Frontier-Class Multimodal LLMs
Paper • 2409.11402 • Published • 75 -
BRAVE: Broadening the visual encoding of vision-language models
Paper • 2404.07204 • Published • 19 -
Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
Paper • 2403.18814 • Published • 48 -
Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Multimodal Models
Paper • 2409.17146 • Published • 122
-
Writing in the Margins: Better Inference Pattern for Long Context Retrieval
Paper • 2408.14906 • Published • 143 -
Training Language Models to Self-Correct via Reinforcement Learning
Paper • 2409.12917 • Published • 141 -
Towards a Unified View of Preference Learning for Large Language Models: A Survey
Paper • 2409.02795 • Published • 74 -
Attention Heads of Large Language Models: A Survey
Paper • 2409.03752 • Published • 93