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An open API service indexing awesome lists of open source software.
awesome-cv-best-paper-list
Awesome Best Papers in Computer Vision Top Conference(CVPR/ICCV/ECCV)
https://github.com/yyyujintang/awesome-cv-best-paper-list
Last synced: 3 days ago
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CVPR Best Student Paper Award
- “Mip-Splatting: Alias-free 3D Gaussian Splatting” - splatting)  | |
- “3D Registration With Maximal Cliques” - Registration-with-Maximal-Cliques)  | |
- “EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation” - cprg/EPro-PnP)  | |
- “Task Programming: Learning Data Efficient Behavior Representations” - programming) |
- “BioCLIP: A Vision Foundation Model for the Tree of Life” - Wolf, W.-L. Chao, Y. Su | | |
- “BSP-Net: Generating Compact Meshes via Binary Space Partitioning” - NET-original)  | [Demo](https://bsp-net.github.io/) |
- “Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation” - F. Wang, W. Y. Wang, L. Zhang | | |
- “BioCLIP: A Vision Foundation Model for the Tree of Life” - Wolf, W.-L. Chao, Y. Su | | |
- “Mip-Splatting: Alias-free 3D Gaussian Splatting” - splatting)  | |
- “3D Registration With Maximal Cliques” - Registration-with-Maximal-Cliques)  | |
- “EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation” - cprg/EPro-PnP)  | |
- “Task Programming: Learning Data Efficient Behavior Representations” - programming) |
- “BSP-Net: Generating Compact Meshes via Binary Space Partitioning” - NET-original)  | [Demo](https://bsp-net.github.io/) |
- “Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation” - F. Wang, W. Y. Wang, L. Zhang | | |
- “Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies” - Capture)  | [Demo](https://www.cs.cmu.edu/~hanbyulj/totalcapture/) |
- “Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies” - Capture)  | [Demo](https://www.cs.cmu.edu/~hanbyulj/totalcapture/) |
- “Computational Imaging on the Electric Grid”
- “Computational Imaging on the Electric Grid”
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CVPR Best Paper Award
- “Deep Residual Learning for Image Recognition”
- "Rich Human Feedback for Text-to-Image Generation" - Tuset, S. Young, F. Yang, J. Ke, K. D. Dvijotham, K. M. Collins, Y. Luo, Y. Li, K. J. Kohlhoff, D. Ramachandran, V. Navalpakkam | [Code](https://github.com/google-research/google-research/tree/master/richhf_18k) | |
- "Generative Image Dynamics"
- "Visual Programming: Compositional Visual Reasoning Without Training"
- “Planning-Oriented Autonomous Driving”
- “Learning to Solve Hard Minimal Problems”
- "Visual Programming: Compositional Visual Reasoning Without Training"
- “Planning-Oriented Autonomous Driving”
- “Learning to Solve Hard Minimal Problems”
- “GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields” - niemeyer.github.io/project-pages/giraffe/index.html) |
- “Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild” - learning-of-probably-symmetric-deformable-3d-objects-from-images-in-the-wild.html) |
- “A Theory of Fermat Paths for Non-Line-of-Sight Shape Reconstruction” - ci-lab/nlos_fermat_path)  | [Demo](https://imaging.cs.cmu.edu/fermat_paths/) |
- “Taskonomy: Disentangling Task Transfer Learning”
- "Rich Human Feedback for Text-to-Image Generation" - Tuset, S. Young, F. Yang, J. Ke, K. D. Dvijotham, K. M. Collins, Y. Luo, Y. Li, K. J. Kohlhoff, D. Ramachandran, V. Navalpakkam | [Code](https://github.com/google-research/google-research/tree/master/richhf_18k) | |
- "Generative Image Dynamics"
- “Densely Connected Convolutional Networks”
- “Learning from Simulated and Unsupervised Images through Adversarial Training”
- “Deep Residual Learning for Image Recognition”
- “GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields” - niemeyer.github.io/project-pages/giraffe/index.html) |
- “Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild” - learning-of-probably-symmetric-deformable-3d-objects-from-images-in-the-wild.html) |
- “A Theory of Fermat Paths for Non-Line-of-Sight Shape Reconstruction” - ci-lab/nlos_fermat_path)  | [Demo](https://imaging.cs.cmu.edu/fermat_paths/) |
- “Taskonomy: Disentangling Task Transfer Learning”
- “Densely Connected Convolutional Networks”
- “Learning from Simulated and Unsupervised Images through Adversarial Training”
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CVPR Best Paper Honorable Mention Award
- “DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation”
- “DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation”
- “DynIBaR: Neural Dynamic Image-Based Rendering”
- “DynIBaR: Neural Dynamic Image-Based Rendering”
- “Dual-Shutter Optical Vibration Sensing”
- “Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields” - research/multinerf)  | [Demo](https://dorverbin.github.io/refnerf/) |
- “Exploring Simple Siamese Representation Learning”
- “Learning High Fidelity Depths of Dressed Humans by Watching Social Media Dance Videos”
- “Dual-Shutter Optical Vibration Sensing”
- “Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields” - research/multinerf)  | [Demo](https://dorverbin.github.io/refnerf/) |
- “Exploring Simple Siamese Representation Learning”
- “Learning High Fidelity Depths of Dressed Humans by Watching Social Media Dance Videos”
- “Less is More: ClipBERT for Video-and-Language Learning via Sparse Sampling”
- “Binary TTC: A Temporal Geofence for Autonomous Navigation”
- “Real-Time High-Resolution Background Matting” - Shlizerman | [Code](https://github.com/PeterL1n/BackgroundMattingV2)  | [Demo](https://grail.cs.washington.edu/projects/background-matting-v2/#/) |
- “Less is More: ClipBERT for Video-and-Language Learning via Sparse Sampling”
- “Binary TTC: A Temporal Geofence for Autonomous Navigation”
- “A Style-Based Generator Architecture for Generative Adversarial Networks”
- “Learning the Depths of Moving People by Watching Frozen People” - depth.github.io/) |
- “Real-Time High-Resolution Background Matting” - Shlizerman | [Code](https://github.com/PeterL1n/BackgroundMattingV2)  | [Demo](https://grail.cs.washington.edu/projects/background-matting-v2/#/) |
- “DeepCap: Monocular Human Performance Capture Using Weak Supervision” - Moll, Christian Theobalt | | |
- “Learning the Depths of Moving People by Watching Frozen People” - depth.github.io/) |
- “Deep Learning of Graph Matching”
- “DeepCap: Monocular Human Performance Capture Using Weak Supervision” - Moll, Christian Theobalt | | |
- “A Style-Based Generator Architecture for Generative Adversarial Networks”
- “CodeSLAM — Learning a Compact, Optimisable Representation for Dense Visual SLAM”
- “SPLATNet: Sparse Lattice Networks for Point Cloud Processing” - H. Yang, and J. Kautz | [Code](https://github.com/NVlabs/splatnet)  | |
- “CodeSLAM — Learning a Compact, Optimisable Representation for Dense Visual SLAM”
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