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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 • 45 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 24
Collections
Discover the best community collections!
Collections including paper arxiv:2407.07726
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Adapting Large Language Models via Reading Comprehension
Paper • 2309.09530 • Published • 81 -
Gemma: Open Models Based on Gemini Research and Technology
Paper • 2403.08295 • Published • 51 -
Simple and Scalable Strategies to Continually Pre-train Large Language Models
Paper • 2403.08763 • Published • 52 -
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism
Paper • 2401.02954 • Published • 49
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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
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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
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Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 24 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 84 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 152 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 24
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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
-
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 • 45 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 24
-
Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 24 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 84 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 152 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 24
-
Adapting Large Language Models via Reading Comprehension
Paper • 2309.09530 • Published • 81 -
Gemma: Open Models Based on Gemini Research and Technology
Paper • 2403.08295 • Published • 51 -
Simple and Scalable Strategies to Continually Pre-train Large Language Models
Paper • 2403.08763 • Published • 52 -
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism
Paper • 2401.02954 • Published • 49
-
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
-
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