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Collections including paper arxiv:2403.07420

EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters

Paper • 2402.04252 • Published Feb 6 • 25
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models

Paper • 2402.03749 • Published Feb 6 • 12
ScreenAI: A Vision-Language Model for UI and Infographics Understanding

Paper • 2402.04615 • Published Feb 7 • 36
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss

Paper • 2402.05008 • Published Feb 7 • 19

How Far Are We from Intelligent Visual Deductive Reasoning?

Paper • 2403.04732 • Published Mar 7 • 18
MoAI: Mixture of All Intelligence for Large Language and Vision Models

Paper • 2403.07508 • Published Mar 12 • 75
DragAnything: Motion Control for Anything using Entity Representation

Paper • 2403.07420 • Published Mar 12 • 12
Learning and Leveraging World Models in Visual Representation Learning

Paper • 2403.00504 • Published Mar 1 • 29

Papers - Video - Entity Recognition

DragAnything: Motion Control for Anything using Entity Representation

Paper • 2403.07420 • Published Mar 12 • 12
Capabilities of Gemini Models in Medicine

Paper • 2404.18416 • Published Apr 29 • 22
MMWorld: Towards Multi-discipline Multi-faceted World Model Evaluation in Videos

Paper • 2406.08407 • Published Jun 12 • 24

Papers - Video - Motion Control

DragAnything: Motion Control for Anything using Entity Representation

Paper • 2403.07420 • Published Mar 12 • 12

Video as the New Language for Real-World Decision Making

Paper • 2402.17139 • Published Feb 27 • 18
VideoCrafter1: Open Diffusion Models for High-Quality Video Generation

Paper • 2310.19512 • Published Oct 30, 2023 • 15
VideoMamba: State Space Model for Efficient Video Understanding

Paper • 2403.06977 • Published Mar 11 • 27
VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Paper • 2401.09047 • Published Jan 17 • 13

MagicVideo-V2: Multi-Stage High-Aesthetic Video Generation

Paper • 2401.04468 • Published Jan 9 • 47
VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Paper • 2401.09047 • Published Jan 17 • 13
AnimateLCM: Accelerating the Animation of Personalized Diffusion Models and Adapters with Decoupled Consistency Learning

Paper • 2402.00769 • Published Feb 1 • 20
Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization

Paper • 2402.03161 • Published Feb 5 • 14

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