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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 • 14 -
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:2511.08923
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TiDAR: Think in Diffusion, Talk in Autoregression
Paper • 2511.08923 • Published • 121 -
Diffusion Language Models are Super Data Learners
Paper • 2511.03276 • Published • 128 -
What Makes Diffusion Language Models Super Data Learners?
Paper • 2510.04071 • Published -
LLaDA2.0: Scaling Up Diffusion Language Models to 100B
Paper • 2512.15745 • Published • 78
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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 • 14 -
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
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TiDAR: Think in Diffusion, Talk in Autoregression
Paper • 2511.08923 • Published • 121 -
Diffusion Language Models are Super Data Learners
Paper • 2511.03276 • Published • 128 -
What Makes Diffusion Language Models Super Data Learners?
Paper • 2510.04071 • Published -
LLaDA2.0: Scaling Up Diffusion Language Models to 100B
Paper • 2512.15745 • Published • 78