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Compose and Conquer: Diffusion-Based 3D Depth Aware Composable Image Synthesis
Paper • 2401.09048 • Published • 10 -
Improving fine-grained understanding in image-text pre-training
Paper • 2401.09865 • Published • 18 -
Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
Paper • 2401.10891 • Published • 62 -
Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild
Paper • 2401.13627 • Published • 78
Collections
Discover the best community collections!
Collections including paper arxiv:2404.01292
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MambaMixer: Efficient Selective State Space Models with Dual Token and Channel Selection
Paper • 2403.19888 • Published • 12 -
Measuring Style Similarity in Diffusion Models
Paper • 2404.01292 • Published • 16 -
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
Paper • 1804.07461 • Published • 4 -
MoDE: CLIP Data Experts via Clustering
Paper • 2404.16030 • Published • 15
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Compose and Conquer: Diffusion-Based 3D Depth Aware Composable Image Synthesis
Paper • 2401.09048 • Published • 10 -
Improving fine-grained understanding in image-text pre-training
Paper • 2401.09865 • Published • 18 -
Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
Paper • 2401.10891 • Published • 62 -
Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild
Paper • 2401.13627 • Published • 78
-
MambaMixer: Efficient Selective State Space Models with Dual Token and Channel Selection
Paper • 2403.19888 • Published • 12 -
Measuring Style Similarity in Diffusion Models
Paper • 2404.01292 • Published • 16 -
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
Paper • 1804.07461 • Published • 4 -
MoDE: CLIP Data Experts via Clustering
Paper • 2404.16030 • Published • 15