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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:2406.09415
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Depth Anything V2
Paper • 2406.09414 • Published • 104 -
An Image is Worth More Than 16x16 Patches: Exploring Transformers on Individual Pixels
Paper • 2406.09415 • Published • 52 -
Physics3D: Learning Physical Properties of 3D Gaussians via Video Diffusion
Paper • 2406.04338 • Published • 40 -
SAM 2: Segment Anything in Images and Videos
Paper • 2408.00714 • Published • 116
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Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models
Paper • 2402.14848 • Published • 20 -
The Prompt Report: A Systematic Survey of Prompting Techniques
Paper • 2406.06608 • Published • 66 -
CRAG -- Comprehensive RAG Benchmark
Paper • 2406.04744 • Published • 49 -
Transformers meet Neural Algorithmic Reasoners
Paper • 2406.09308 • Published • 45
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An Image is Worth More Than 16x16 Patches: Exploring Transformers on Individual Pixels
Paper • 2406.09415 • Published • 52 -
4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities
Paper • 2406.09406 • Published • 15 -
VideoGUI: A Benchmark for GUI Automation from Instructional Videos
Paper • 2406.10227 • Published • 9 -
What If We Recaption Billions of Web Images with LLaMA-3?
Paper • 2406.08478 • Published • 42
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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
-
An Image is Worth More Than 16x16 Patches: Exploring Transformers on Individual Pixels
Paper • 2406.09415 • Published • 52 -
4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities
Paper • 2406.09406 • Published • 15 -
VideoGUI: A Benchmark for GUI Automation from Instructional Videos
Paper • 2406.10227 • Published • 9 -
What If We Recaption Billions of Web Images with LLaMA-3?
Paper • 2406.08478 • Published • 42
-
Depth Anything V2
Paper • 2406.09414 • Published • 104 -
An Image is Worth More Than 16x16 Patches: Exploring Transformers on Individual Pixels
Paper • 2406.09415 • Published • 52 -
Physics3D: Learning Physical Properties of 3D Gaussians via Video Diffusion
Paper • 2406.04338 • Published • 40 -
SAM 2: Segment Anything in Images and Videos
Paper • 2408.00714 • Published • 116
-
Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models
Paper • 2402.14848 • Published • 20 -
The Prompt Report: A Systematic Survey of Prompting Techniques
Paper • 2406.06608 • Published • 66 -
CRAG -- Comprehensive RAG Benchmark
Paper • 2406.04744 • Published • 49 -
Transformers meet Neural Algorithmic Reasoners
Paper • 2406.09308 • Published • 45