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https://github.com/SkalskiP/top-cvpr-2023-papers
This repository is a curated collection of the most exciting and influential CVPR 2023 papers. 🔥 [Paper + Code]
https://github.com/SkalskiP/top-cvpr-2023-papers
Last synced: 3 months ago
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This repository is a curated collection of the most exciting and influential CVPR 2023 papers. 🔥 [Paper + Code]
- Host: GitHub
- URL: https://github.com/SkalskiP/top-cvpr-2023-papers
- Owner: SkalskiP
- Created: 2023-06-15T12:21:15.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-06-18T17:53:34.000Z (5 months ago)
- Last Synced: 2024-06-21T18:56:01.377Z (5 months ago)
- Language: Python
- Homepage:
- Size: 33.2 KB
- Stars: 630
- Watchers: 14
- Forks: 67
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- AiTreasureBox - CVPR 2023 papers
README
## 👋 hello
Computer 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.
## 🗞️ papers
| **topic** | **title** | **repository / paper** |
|:---------:|:---------:|:----------------------:|
| Segmentation | OneFormer: One Transformer To Rule Universal Image Segmentation | [](https://github.com/SHI-Labs/OneFormer) [](https://arxiv.org/abs/2211.06220)|
| Segmentation | X-Decoder: Generalized Decoding for Pixel, Image and Language | [](https://github.com/microsoft/X-Decoder) [](https://arxiv.org/abs/2212.11270)|
| Segmentation and Generative AI | Images Speak in Images: A Generalist Painter for In-Context Visual Learning | [](https://github.com/baaivision/Painter) [](https://arxiv.org/abs/2212.02499)|
| Segmentation | PACO: Parts and Attributes of Common Objects | [](https://github.com/facebookresearch/paco) [](https://arxiv.org/abs/2301.01795)|
| Segmentation | Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP | [](https://github.com/facebookresearch/ov-seg) [](https://arxiv.org/abs/2210.04150)|
| NeRF | DynIBaR: Neural Dynamic Image-Based Rendering | [](https://github.com/google/dynibar) [](https://arxiv.org/abs/2211.11082)|
| 3D | Vid2Avatar: 3D Avatar Reconstruction from Videos in the Wild via Self-supervised Scene Decomposition | [](https://github.com/MoyGcc/vid2avatar) [](https://arxiv.org/abs/2302.11566)|
| Generative AI | 3D-aware Conditional Image Synthesis | [](https://github.com/dunbar12138/pix2pix3d) [](https://arxiv.org/abs/2302.08509)|
| 3D | 3D Human Mesh Estimation from Virtual Markers | [](https://github.com/ShirleyMaxx/VirtualMarker) [](https://arxiv.org/abs/2303.11726)|
| Transfer Learning | A Data-Based Perspective on Transfer Learning | [](https://github.com/MadryLab/data-transfer) [](https://arxiv.org/abs/2207.05739)|
| Segmentation | Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models | [](https://github.com/NVlabs/ODISE) [](https://arxiv.org/abs/2303.04803)|
| Generative AI | DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation | [](https://github.com/google/dreambooth) [](https://arxiv.org/abs/2208.12242)|
| Generative AI | InstructPix2Pix: Learning to Follow Image Editing Instructions | [](https://github.com/timothybrooks/instruct-pix2pix) [](https://arxiv.org/abs/2211.09800)|
| Generative AI | High-resolution image reconstruction with latent diffusion models from human brain activity | [](https://github.com/yu-takagi/StableDiffusionReconstruction) [](https://arxiv.org/abs/2306.11536)|
| Benchmarking | Beyond mAP: Towards better evaluation of instance segmentation | [](https://github.com/rohitrango/beyond-map) [](https://arxiv.org/abs/2207.01614)|
| NeRF | SPIn-NeRF: Multiview Segmentation and Perceptual Inpainting with Neural Radiance Fields | [](https://github.com/SamsungLabs/SPIn-NeRF) [](https://arxiv.org/abs/2211.12254)|
| 3D | Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild | [](https://github.com/facebookresearch/omni3d) [](https://arxiv.org/abs/2207.10660)|
| 3D | ECON: Explicit Clothed humans Optimized via Normal integration | [](https://github.com/YuliangXiu/ECON) [](https://arxiv.org/abs/2212.07422)|
| 3D | NeuralLift-360: Lifting An In-the-wild 2D Photo to A 3D Object with 360° Views | [](https://github.com/VITA-Group/NeuralLift-360) [](https://arxiv.org/abs/2211.16431)|
## 🦸 contribution
We would love your help in making this repository even better! If you know of an amazing paper that isn't listed
here, or if you have any suggestions for improvement, feel free to open an
[issue](https://github.com/SkalskiP/top-cvpr-2023-papers/issues) or submit a
[pull request](https://github.com/SkalskiP/top-cvpr-2023-papers/pulls).