{"id":15623722,"url":"https://github.com/skalskip/top-cvpr-2023-papers","last_synced_at":"2025-04-04T07:06:52.691Z","repository":{"id":176396651,"uuid":"654115596","full_name":"SkalskiP/top-cvpr-2023-papers","owner":"SkalskiP","description":"This repository is a curated collection of the most exciting and influential CVPR 2023 papers. 🔥 [Paper + Code]","archived":false,"fork":false,"pushed_at":"2024-07-03T09:40:43.000Z","size":38,"stargazers_count":645,"open_issues_count":0,"forks_count":65,"subscribers_count":12,"default_branch":"master","last_synced_at":"2025-03-28T06:05:39.289Z","etag":null,"topics":["computer-vision","cvpr","cvpr2023","image-segmentation","object-detection","paper","transformers","vision-and-language"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"cc0-1.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/SkalskiP.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-06-15T12:21:15.000Z","updated_at":"2025-03-26T12:54:17.000Z","dependencies_parsed_at":null,"dependency_job_id":"8bb8e1a5-e24f-400f-b8a0-9309436a6042","html_url":"https://github.com/SkalskiP/top-cvpr-2023-papers","commit_stats":null,"previous_names":["skalskip/top-cvpr-2023-papers"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SkalskiP%2Ftop-cvpr-2023-papers","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SkalskiP%2Ftop-cvpr-2023-papers/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SkalskiP%2Ftop-cvpr-2023-papers/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SkalskiP%2Ftop-cvpr-2023-papers/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SkalskiP","download_url":"https://codeload.github.com/SkalskiP/top-cvpr-2023-papers/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247135144,"owners_count":20889421,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["computer-vision","cvpr","cvpr2023","image-segmentation","object-detection","paper","transformers","vision-and-language"],"created_at":"2024-10-03T09:58:28.553Z","updated_at":"2025-04-04T07:06:52.658Z","avatar_url":"https://github.com/SkalskiP.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"![visitor badge](https://visitor-badge.laobi.icu/badge?page_id=SkalskiP.top-cvpr-2023-papers)\n\n\u003cdiv align=\"center\"\u003e\n  \u003ch1 align=\"center\"\u003etop CVPR 2023 papers\u003c/h1\u003e\n  \u003ca href=\"https://github.com/SkalskiP/top-cvpr-2024-papers\"\u003e2024\u003c/a\u003e\n\u003c/div\u003e\n\n\u003cp align=\"center\"\u003e\n    \u003c/br\u003e\n    \u003cimg width=\"600\" src=\"https://github.com/SkalskiP/top-cvpr-2023-papers/assets/26109316/2d7be39e-11a0-4298-ad90-c0645af0c5ac\" alt=\"vancouver\"\u003e\n    \u003c/br\u003e\n\u003c/p\u003e\n\n## 👋 hello\n\nComputer Vision and Pattern Recognition is a massive conference. In **2023** alone, **9,155** papers were submitted, and **2,359** were accepted. I created this repository to help you search for crème de la crème of CVPR publications. If the paper you are looking for is not on my short list, take a peek at the full [list](https://cvpr.thecvf.com/Conferences/2023/AcceptedPapers) of accepted papers.\n\n## 🗞️ papers\n\n\u003c!--- AUTOGENERATED_COURSES_TABLE --\u003e\n\u003c!---\n   WARNING: DO NOT EDIT THIS TABLE MANUALLY. IT IS AUTOMATICALLY GENERATED.\n   HEAD OVER TO CONTRIBUTING.MD FOR MORE DETAILS ON HOW TO MAKE CHANGES PROPERLY.\n--\u003e\n| **topic** | **title** | **repository / paper** |\n|:---------:|:---------:|:----------------------:|\n| Segmentation | OneFormer: One Transformer To Rule Universal Image Segmentation |  [![GitHub](https://img.shields.io/github/stars/SHI-Labs/OneFormer?style=social)](https://github.com/SHI-Labs/OneFormer) [![arXiv](https://img.shields.io/badge/arXiv-2211.06220-b31b1b.svg)](https://arxiv.org/abs/2211.06220)|\n| Segmentation | X-Decoder: Generalized Decoding for Pixel, Image and Language |  [![GitHub](https://img.shields.io/github/stars/microsoft/X-Decoder?style=social)](https://github.com/microsoft/X-Decoder) [![arXiv](https://img.shields.io/badge/arXiv-2212.11270-b31b1b.svg)](https://arxiv.org/abs/2212.11270)|\n| Segmentation and Generative AI | Images Speak in Images: A Generalist Painter for In-Context Visual Learning |  [![GitHub](https://img.shields.io/github/stars/baaivision/Painter?style=social)](https://github.com/baaivision/Painter) [![arXiv](https://img.shields.io/badge/arXiv-2212.02499-b31b1b.svg)](https://arxiv.org/abs/2212.02499)|\n| Segmentation | PACO: Parts and Attributes of Common Objects |  [![GitHub](https://img.shields.io/github/stars/facebookresearch/paco?style=social)](https://github.com/facebookresearch/paco) [![arXiv](https://img.shields.io/badge/arXiv-2301.01795-b31b1b.svg)](https://arxiv.org/abs/2301.01795)|\n| Segmentation | Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP |  [![GitHub](https://img.shields.io/github/stars/facebookresearch/ov-seg?style=social)](https://github.com/facebookresearch/ov-seg) [![arXiv](https://img.shields.io/badge/arXiv-2210.04150-b31b1b.svg)](https://arxiv.org/abs/2210.04150)|\n| NeRF | DynIBaR: Neural Dynamic Image-Based Rendering |  [![GitHub](https://img.shields.io/github/stars/google/dynibar?style=social)](https://github.com/google/dynibar) [![arXiv](https://img.shields.io/badge/arXiv-2211.11082-b31b1b.svg)](https://arxiv.org/abs/2211.11082)|\n| 3D | Vid2Avatar: 3D Avatar Reconstruction from Videos in the Wild via Self-supervised Scene Decomposition |  [![GitHub](https://img.shields.io/github/stars/MoyGcc/vid2avatar?style=social)](https://github.com/MoyGcc/vid2avatar) [![arXiv](https://img.shields.io/badge/arXiv-2302.11566-b31b1b.svg)](https://arxiv.org/abs/2302.11566)|\n| Generative AI | 3D-aware Conditional Image Synthesis |  [![GitHub](https://img.shields.io/github/stars/dunbar12138/pix2pix3d?style=social)](https://github.com/dunbar12138/pix2pix3d) [![arXiv](https://img.shields.io/badge/arXiv-2302.08509-b31b1b.svg)](https://arxiv.org/abs/2302.08509)|\n| 3D | 3D Human Mesh Estimation from Virtual Markers |  [![GitHub](https://img.shields.io/github/stars/ShirleyMaxx/VirtualMarker?style=social)](https://github.com/ShirleyMaxx/VirtualMarker) [![arXiv](https://img.shields.io/badge/arXiv-2303.11726-b31b1b.svg)](https://arxiv.org/abs/2303.11726)|\n| Transfer Learning | A Data-Based Perspective on Transfer Learning |  [![GitHub](https://img.shields.io/github/stars/MadryLab/data-transfer?style=social)](https://github.com/MadryLab/data-transfer) [![arXiv](https://img.shields.io/badge/arXiv-2207.05739-b31b1b.svg)](https://arxiv.org/abs/2207.05739)|\n| Segmentation | Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models |  [![GitHub](https://img.shields.io/github/stars/NVlabs/ODISE?style=social)](https://github.com/NVlabs/ODISE) [![arXiv](https://img.shields.io/badge/arXiv-2303.04803-b31b1b.svg)](https://arxiv.org/abs/2303.04803)|\n| Generative AI | DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation |  [![GitHub](https://img.shields.io/github/stars/google/dreambooth?style=social)](https://github.com/google/dreambooth) [![arXiv](https://img.shields.io/badge/arXiv-2208.12242-b31b1b.svg)](https://arxiv.org/abs/2208.12242)|\n| Generative AI | InstructPix2Pix: Learning to Follow Image Editing Instructions |  [![GitHub](https://img.shields.io/github/stars/timothybrooks/instruct-pix2pix?style=social)](https://github.com/timothybrooks/instruct-pix2pix) [![arXiv](https://img.shields.io/badge/arXiv-2211.09800-b31b1b.svg)](https://arxiv.org/abs/2211.09800)|\n| Generative AI | High-resolution image reconstruction with latent diffusion models from human brain activity |  [![GitHub](https://img.shields.io/github/stars/yu-takagi/StableDiffusionReconstruction?style=social)](https://github.com/yu-takagi/StableDiffusionReconstruction) [![arXiv](https://img.shields.io/badge/arXiv-2306.11536-b31b1b.svg)](https://arxiv.org/abs/2306.11536)|\n| Benchmarking | Beyond mAP: Towards better evaluation of instance segmentation |  [![GitHub](https://img.shields.io/github/stars/rohitrango/beyond-map?style=social)](https://github.com/rohitrango/beyond-map) [![arXiv](https://img.shields.io/badge/arXiv-2207.01614-b31b1b.svg)](https://arxiv.org/abs/2207.01614)|\n| NeRF | SPIn-NeRF: Multiview Segmentation and Perceptual Inpainting with Neural Radiance Fields |  [![GitHub](https://img.shields.io/github/stars/SamsungLabs/SPIn-NeRF?style=social)](https://github.com/SamsungLabs/SPIn-NeRF) [![arXiv](https://img.shields.io/badge/arXiv-2211.12254-b31b1b.svg)](https://arxiv.org/abs/2211.12254)|\n| 3D | Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild |  [![GitHub](https://img.shields.io/github/stars/facebookresearch/omni3d?style=social)](https://github.com/facebookresearch/omni3d) [![arXiv](https://img.shields.io/badge/arXiv-2207.10660-b31b1b.svg)](https://arxiv.org/abs/2207.10660)|\n| 3D | ECON: Explicit Clothed humans Optimized via Normal integration |  [![GitHub](https://img.shields.io/github/stars/YuliangXiu/ECON?style=social)](https://github.com/YuliangXiu/ECON) [![arXiv](https://img.shields.io/badge/arXiv-2212.07422-b31b1b.svg)](https://arxiv.org/abs/2212.07422)|\n| 3D | NeuralLift-360: Lifting An In-the-wild 2D Photo to A 3D Object with 360° Views |  [![GitHub](https://img.shields.io/github/stars/VITA-Group/NeuralLift-360?style=social)](https://github.com/VITA-Group/NeuralLift-360) [![arXiv](https://img.shields.io/badge/arXiv-2211.16431-b31b1b.svg)](https://arxiv.org/abs/2211.16431)|\n\u003c!--- AUTOGENERATED_COURSES_TABLE --\u003e\n\n## 🦸 contribution\n\nWe would love your help in making this repository even better! If you know of an amazing paper that isn't listed\nhere, or if you have any suggestions for improvement, feel free to open an\n[issue](https://github.com/SkalskiP/top-cvpr-2023-papers/issues) or submit a\n[pull request](https://github.com/SkalskiP/top-cvpr-2023-papers/pulls).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fskalskip%2Ftop-cvpr-2023-papers","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fskalskip%2Ftop-cvpr-2023-papers","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fskalskip%2Ftop-cvpr-2023-papers/lists"}