https://github.com/cvhub520/awesome-computer-vision
https://github.com/cvhub520/awesome-computer-vision
List: awesome-computer-vision
Last synced: 5 months ago
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- Host: GitHub
- URL: https://github.com/cvhub520/awesome-computer-vision
- Owner: CVHub520
- Created: 2023-03-11T15:33:04.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-03-26T16:38:47.000Z (about 3 years ago)
- Last Synced: 2025-10-19T09:46:50.694Z (8 months ago)
- Size: 29.3 KB
- Stars: 4
- Watchers: 4
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## 关注我们
门头沟学院AI视觉实验室御用公众号 | 学术 | 科研 | 就业
扫描下方二维码，然后回复关键词“进群”，即可加入“读者交流群”
Awesome Computer Vision [](https://awesome.re)
This is a list of computer vison related resources.
The timeline follows the time of arxiv.org publication.
Please feel free to [pull requests](https://github.com/CVHub520/awesome-computer-vision/pulls) or [open an issue](https://github.com/CVHub520/awesome-computer-vision/issues) to add papers.
---
Table of Contents
- [Awesome Classification](#awesome-classification)
- [Awesome Segmentation](#awesome-segmentation)
- [Awesome Detection](#awesome-detection)
- [Awesome Low Level Vison](#awesome-low-level-vison)
- [Awesome 3D Vision](#awesome-3d-vision)
- [Awesome 3D Classification & Detection & Segmentation](#awesome-3d-classification--detection--segmentation)
- [Awesome 3D Representations](#awesome-3d-representations)
- [Awesome 3D Reconstruction](#awesome-3d-reconstruction)
- [Awesome Depth & Flow Estimation](#awesome-depth--flow-estimation)
- [Awesome Neural Rendering](#awesome-neural-rendering)
- [Awesome SLAM](#awesome-slam)
- [Awesome Diffusion](#awesome-diffusion)
- [Awesome Network Architectures And Techniques](#awesome-network-architectures-and-techniques)
- [Awesome Optimization Methods](#awesome-optimization-methods)
- [Awesome Face Recognition](#awesome-face-recognition)
# Awesome Classification
## Articles And Interpretation
| Type | 简称 | | | | | |
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| | | | | | | |
| | CNN | Trans | CNN&Trans | RNN | GEN | Other |
| 网络类型 | CNN | Transformer | CNN + Transformer | RNN | GAN/Diffusion | GNN... |
| | | | | | | |
| | | | | | | |
| | Sup | UnSup | Self | Semi | FSL | ZSL |
| 监督方式 | Supervised Learning | Unsupervised Learning | Self-Supervised Learning | Semi-Supervised Learning | Few-shot Learning | Zero-shot Learning |
| | | | | | | |
| | | | | | | |
| | FGC | | - | - | - | - |
| 任务类型 | Fine-Grained Classification | | -| - | -| - |
| | | | | | | |
| | | | | | | |
| | Medical |-| - | - | - | Other |
| 其他特性 | Medical Image Classification | - | -| - | - | - |
| Paper | Publication | Type | 性能 | 代码 | 解读 |
| :- | :---| :---:| :---: | :---: | :---: |
|Shape-Aware Fine-Grained Classification of Erythroid Cells | | __`CNN&Trans`__ __`Sup`__ __`FGC`__ __`Medical`__ | |[GitHub](https://github.com/wangye8899/bmec) | [BMEC：基于形状感知的红细胞细粒度分类](https://mp.weixin.qq.com/s/zr6ODsmj85JsnQ1Uoq2xdg) |
## Blogs
## Libraies
# Awesome Segmentation
## Articles And Interpretation
| Type | 简称 | | | | | |
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| | | | | | | |
| | CNN | Trans | CNN&Trans | RNN | GEN | Other |
| 网络类型 | CNN | Transformer | CNN + Transformer | RNN | GAN/Diffusion | GNN... |
| | | | | | | |
| | | | | | | |
| | Sup | UnSup | Self | Semi | Meta | Other |
| 监督方式 | Supervised Learning | Unsupervised Learning | Self-Supervised Learning | Semi-Supervised Learning | Meta Learning | Weakly-Supervised... |
| | | | | | | |
| | | | | | | |
| | Semantic | Instance | Video | Medical | Aut | Other |
| 任务类型 | Semantic Segmentation | Instance Segmentation | Video Segmentation | Medical Segmentation | Autonomous Driving Segmentation | Saliency Segmentation... |
| | | | | | | |
| | | | | | | |
| | MultiModal | UH-Res | Adv | - | - | Other |
| 其他特性 | MultiModal Learning | Ultra-high Resolution | Adversarial Training | - | - | - |
| Paper | Publication | Type | 性能 | 代码 | 解读 |
| :- | :---| :---:| :---: | :---: | :---: |
|Delivering Arbitrary-Modal Semantic Segmentation | CVPR 2023 | __`CNN&Trans`__ __`Sup`__ __`Semantic`__ __`MultiModal`__ | |[GitHub](https://github.com/jamycheung/DELIVER) | [基于编解码架构的强大语义分割基线，解锁多模态语义分割的正确姿势](https://zhuanlan.zhihu.com/p/613293738) |
| ISDNet: Integrating Shallow and Deep Networks for Efficient Ultra-high Resolution Segmentation | CVPR 2022 | __`CNN`__ __`Sup`__ __`Semantic`__ __`UH-Res`__ | | [GitHub](https://github.com/cedricgsh/ISDNet) | [探索超高分辨率图像分割的高效之道](https://zhuanlan.zhihu.com/p/611138087) |
| Multi-modal contrastive mutual learning and pseudo-label re-learning for semi-supervised medical image segmentation | MIA 2023 | __`CNN`__ __`Semi`__ __`Medical`__ __`MultiModal`__ | |- | [用于半监督医学图像分割的多模态对比互学习和伪标签再学习方法](https://mp.weixin.qq.com/s/r_5g9I4MGoydpZ2pOix1sA) |
| Class-Aware Adversarial Transformers for Medical Image Segmentation | NeuraIPS 2022 | __`CNN&Trans`__ __`Sup`__ __`Adv`__ | |- | [最新类别感知对抗Transformer分割网络CASTformer](https://mp.weixin.qq.com/s/qAq54Tg_j9AekmMeMwFnwQ) |
| DAE-Former: Dual Attention-guided Efficient Transformer for Medical Image Segmentation | NeuraIPS 2022 | __`Trans`__ __`Sup`__ __`Medical`__ | |[GitHub](https://github.com/mindflow-institue/daeformer) | [DAE-Former：高效双重注意力引导的Transformer网络称霸医学图像分割任务](https://mp.weixin.qq.com/s/RL45_CKwKAwN9hm1t4vUJA) |
## Blogs
- [语义分割大盘点](http://automl.chalearn.org/)
- [关于语义分割的亿点思考](https://zhuanlan.zhihu.com/p/595753988)
## Libraies
- [mmsegmentation](https://github.com/open-mmlab/mmsegmentation)
- [PaddleSeg](https://github.com/open-mmlab/mmsegmentation)
# Awesome Detection
## Articles And Interpretation
| Type | 简称 | | | | | |
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| | | | | | | |
| | CNN | Trans | CNN&Trans | RNN | GEN | Other |
| 网络类型 | CNN | Transformer | CNN + Transformer | RNN | GAN/Diffusion | GNN... |
| | | | | | | |
| | | | | | | |
| | Sup | UnSup | Self | Semi | FSL | ZSL |
| 监督方式 | Supervised Learning | Unsupervised Learning | Self-Supervised Learning | Semi-Supervised Learning | Few-shot Learning | Zero-shot Learning |
| | | | | | | |
| | | | | | | |
| | Small | Moving | Oriented | Body | Head | Other |
| 任务类型 | Small Object Detection | Moving Object Detection | Oriented Object Detection| Body Detection | Head Detection| License Plate Detection... |
| | | | | | | |
| | | | | | | |
| | RGB-D | Real-Time | Video | OpenWorld | Aerial | Other |
| 其他特性 | RGB-D Salient Object Detection | Real-Time Object Detection | Video Salient Object Detection | Open World Object Detection | Object Detection In Aerial Images | - |
| Paper | Publication | Type | 性能 | 代码 | 解读 |
| :- | :---| :---:| :---: | :---: | :---: |
|Open World DETR: Transformer based Open World Object Detection | CVPR 2022 | __`CNN&Trans`__ __`Sup`__ __`Semi`__ __`OpenWorld`__ | |[GitHub](https://github.com/akshitac8/OW-DETR) | [基于DETR的开放世界目标检测——从入门到喜欢](https://mp.weixin.qq.com/s/AAqc_XuRS9MTyU4nuO_6qw) |
## Blogs
## Libraies
# Awesome 3D Classification
## Articles And Interpretation
| Type | 简称 | | | | | |
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| | | | | | | |
| | CNN | Trans | CNN&Trans | RNN | GEN | Other |
| 网络类型 | CNN | Transformer | CNN + Transformer | RNN | GAN/Diffusion | GNN... |
| | | | | | | |
| | | | | | | |
| | Sup | UnSup | Self | Semi | Meta | Other |
| 监督方式 | Supervised Learning | Unsupervised Learning | Self-Supervised Learning | Semi-Supervised Learning | Meta Learning | Weakly-Supervised... |
| | | | | | | |
| | | | | | | |
| | Semantic | Instance | Video | Medical | Aut | Other |
| 任务类型 | Semantic Segmentation | Instance Segmentation | Video Segmentation | Medical Segmentation | Autonomous Driving Segmentation | Saliency Segmentation... |
| | | | | | | |
| | | | | | | |
| | MultiModal | UH-Res | - | - | - | Other |
| 其他特性 | MultiModal Learning | Ultra-high Resolution | - | - | - | - |
| Paper | Publication | Type | 性能 | 代码 | 解读 |
| :- | :---| :---:| :---: | :---: | :---: |
## Blogs
## Libraies
# Awesome 3D Detection
## Articles And Interpretation
| Type | 简称 | | | | | |
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| | | | | | | |
| | CNN | Trans | CNN&Trans | RNN | GEN | Other |
| 网络类型 | CNN | Transformer | CNN + Transformer | RNN | GAN/Diffusion | GNN... |
| | | | | | | |
| | | | | | | |
| | Sup | UnSup | Self | Semi | Meta | Other |
| 监督方式 | Supervised Learning | Unsupervised Learning | Self-Supervised Learning | Semi-Supervised Learning | Meta Learning | Weakly-Supervised... |
| | | | | | | |
| | | | | | | |
| | Semantic | Instance | Video | Medical | Aut | Other |
| 任务类型 | Semantic Segmentation | Instance Segmentation | Video Segmentation | Medical Segmentation | Autonomous Driving Segmentation | Saliency Segmentation... |
| | | | | | | |
| | | | | | | |
| | MultiModal | UH-Res | - | - | - | Other |
| 其他特性 | MultiModal Learning | Ultra-high Resolution | - | - | - | - |
| Paper | Publication | Type | 性能 | 代码 | 解读 |
| :- | :---| :---:| :---: | :---: | :---: |
## Blogs
## Libraies
# Awesome 3D Segmentation
## Articles And Interpretation
| Type | 简称 | | | | | |
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| | | | | | | |
| | CNN | Trans | CNN&Trans | RNN | GEN | Other |
| 网络类型 | CNN | Transformer | CNN + Transformer | RNN | GAN/Diffusion | GNN... |
| | | | | | | |
| | | | | | | |
| | Sup | UnSup | Self | Semi | Meta | Other |
| 监督方式 | Supervised Learning | Unsupervised Learning | Self-Supervised Learning | Semi-Supervised Learning | Meta Learning | Weakly-Supervised... |
| | | | | | | |
| | | | | | | |
| | Semantic | Instance | Video | Medical | Aut | Other |
| 任务类型 | Semantic Segmentation | Instance Segmentation | Video Segmentation | Medical Segmentation | Autonomous Driving Segmentation | Saliency Segmentation... |
| | | | | | | |
| | | | | | | |
| | MultiModal | UH-Res | - | - | - | Other |
| 其他特性 | MultiModal Learning | Ultra-high Resolution | - | - | - | - |
| Paper | Publication | Type | 性能 | 代码 | 解读 |
| :- | :---| :---:| :---: | :---: | :---: |
| :fire::fire: UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation | CVPR 2022 | __`CNN&Trans`__ __`Sup`__ __`Semantic`__ __`Medical`__ | | [GitHub](https://github.com/Amshaker/unetr_plus_plus) | [UNETR++：轻量级的共享权重Transformer称霸医学图像分割领域](https://mp.weixin.qq.com/s/fcF5WibfFADnMI1TLaNPng) |
## Blogs
## Libraies
# Awesome Low Level Vison
## Articles And Interpretation
## Blogs
## Libraies
# Awesome 3D Vision
# Awesome 3D Representations
## Articles And Interpretation
## Blogs
## Libraies
# Awesome 3D Reconstruction
## Articles And Interpretation
## Blogs
## Libraies
# Awesome Depth & Flow Estimation
## Articles And Interpretation
| Type | 简称 | | | | | |
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| | | | | | | |
| | CNN | Trans | CNN&Trans | RNN | GEN | Other |
| 网络类型 | CNN | Transformer | CNN + Transformer | RNN | GAN/Diffusion | GNN... |
| | | | | | | |
| | | | | | | |
| | Sup | UnSup | Self | Semi | FSL | ZSL |
| 监督方式 | Supervised Learning | Unsupervised Learning | Self-Supervised Learning | Semi-Supervised Learning | Few-shot Learning | Zero-shot Learning |
| | | | | | | |
| | | | | | | |
| | Mono | Stereo | Match | Flow | | |
| 任务类型 | Monocular Depth Estimation | Stereo Depth Estimation | Stereo Match | Optical Flow Estimation | | |
| | | | | | | |
| | | | | | | |
| | Night | Rel | Met | Rel&Met | Out | In |
| 其他特性 | Nighttime | Relative Depth | Metric Depth | Relative & Metric Depth |Outdoor Domain | Indoor Domain |
| Paper | Publication | Type | 性能 | 代码 | 解读 |
| :- | :---| :---:| :---: | :---: | :---: |
| :fire: ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth | - | __`CNN&Trans`__ __`ZSL`__ __`Mono`__ __`Rel&Met`__ | NYU REL 0.075 |[GitHub](https://github.com/isl-org/ZoeDepth) | [第一个结合相对和绝对深度的多模态单目深度估计网络](https://zhuanlan.zhihu.com/p/615166220) |
| Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth Estimation | CVPR 2023 | __`CNN&Trans`__ __`Self`__ __`Mono`__ __`Rel`__ | KITTI REL 0.102 |[GitHub](https://github.com/noahzn/Lite-Mono) | [一种新的轻量级自监督单目深度估计方法](https://zhuanlan.zhihu.com/p/614680720) |
## Blogs
## Libraies
# Awesome Neural Rendering
## Articles And Interpretation
## Blogs
## Libraies
# Awesome SLAM
## Articles And Interpretation
## Blogs
## Libraies
# Awesome Diffusion
## Articles And Interpretation
| Type | 简称 | | | | | |
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| | | | | | | |
| | CNN | Trans | CNN&Trans | RNN | GEN | Other |
| 网络类型 | CNN | Transformer | CNN + Transformer | RNN | GAN/Diffusion | GNN... |
| | | | | | | |
| | | | | | | |
| | Sup | UnSup | Self | Semi | FSL | ZSL |
| 监督方式 | Supervised Learning | Unsupervised Learning | Self-Supervised Learning | Semi-Supervised Learning | Few-shot Learning | Zero-shot Learning |
| | | | | | | |
| | | | | | | |
| | | Lang | T2I | T2V | T23D | I2T | |
| 任务类型 | Language | Text-to-Text | Text-to-Image | Text-to-Video | Text-to-3D | Image-to-Text | |
| | | | | | | |
| | | | | | | |
| | MultiModal| ImgGen |ImgEdit | ILU | | |
| 其他特性 | MultiModal Learning | Image Generation |Image Editing | Image-and-Language Understanding | | |
| Paper | Publication | Type | 性能 | 代码 | 解读 |
| :- | :---| :---:| :---: | :---: | :---: |
| SINE: SINgle Image Editing with Text-to-Image Diffusion Models | CVPR 2023 | __`GEN`__ __`T2I`__ __`Lang`__ __`ImgEdit`__ | |[GitHub](https://github.com/zhang-zx/SINE) | [SINE: 一种基于扩散模型的单图像编辑解决方案](https://mp.weixin.qq.com/s/zZztD_HkXCiCu_3Fc07yTg) |
| InstructPix2Pix: Learning to Follow Image Editing Instructions | CVPR 2023 (Highlight) | __`GEN`__ __`T2I`__ __`Lang`__ __`ImgEdit`__ | |[GitHub](https://github.com/noahzn/Lite-Mono) | [InstructPix2Pix: 一种无需微调新的快速图像编辑方法](https://mp.weixin.qq.com/s/cJtqmpnkSWSMK4BxLLLm7g) |
| Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models | CVPR 2023 (Highlight) | __`GEN`__ __`T2I`__ __`Lang`__ __`ImgEdit`__ | | - | [一文深度剖析扩散模型究竟学到了什么？](https://mp.weixin.qq.com/s/mf-bGv9i7pypHYKbU7U1Ng) |
| Image-and-Language Understanding from Pixels Only | | __`GEN`__ __`I2T`__ __`MultiModal`__ __`ILU`__ | | - | [CLIPPO：利用Transformer建立多模态模型新范式！](https://mp.weixin.qq.com/s/HZcAMMiiduwxUwaBqjWgCw) |
| GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models | | __`GEN`__ __`T2I`__ __`ImgGen`__ | | [GitHub](https://github.com/openai/glide-text2im) | [OpenAI 年度最新力作 GLIDE：新生代文本引导扩散模型](https://mp.weixin.qq.com/s/sDd1A3HBqKgMnrNSooOqcw) |
## Blogs
## Libraies
# Awesome Network Architectures And Techniques
## Articles And Interpretation
| Type | 简称 | | | | | |
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| | | | | | | |
| | CNN | Trans | CNN&Trans | RNN | GEN | Other |
| 网络类型 | CNN | Transformer | CNN + Transformer | RNN | GAN/Diffusion | GNN... |
| | | | | | | |
| | | | | | | |
| | Sup | UnSup | Self | Semi | Meta | Other |
| 监督方式 | Supervised Learning | Unsupervised Learning | Self-Supervised Learning | Semi-Supervised Learning | Meta Learning | Weakly-Supervised... |
| | | | | | | |
| | | | | | | |
| | NAS | Distill | Pruning | Quant | Transfer | Other |
| 任务类型 | Neural Architecture Search | Knowledge Distillation | Network Pruning | Network Quantization | Transfer Learning | Reinforcement Learning... |
| | | | | | | |
| | | | | | | |
| | MultiModal | | - | - | - | Other |
| 其他特性 | MultiModal Learning | | - | - | - | - |
| Paper | Publication | Type | 性能 | 代码 | 解读 |
| :- | :---| :---:| :---: | :---: | :---: |
|Rethinking Vision Transformers for MobileNet Size and Speed | NeurIPs 2022 | __`Trans`__ __`Sup`__ | - |[GitHub](https://github.com/snap-research/EfficientFormer) | [EfficientFormerV2: Transformer家族中的MobileNet](https://mp.weixin.qq.com/s/CWir9KFQ_W34kJ_bMeY5KQ) |
|FlexiViT: One Model for All Patch Sizes | | __`Trans`__ __`Sup`__ | - |[GitHub](https://github.com/google-research/big_vision) | [FlexiViT: 谷歌手把手教你如何灵活切片](https://mp.weixin.qq.com/s/melu4UePAq301dZpjxsL2A) |
|ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders | | __`CNN`__ __`Sup`__ | - |[GitHub](https://github.com/facebookresearch/ConvNeXt-V2) | [ConvNeXt-V2：当 MAE 遇见 ConvNeXt 会碰撞出怎样的火花？](https://mp.weixin.qq.com/s/6msbFRNpsO9BVL7-yi-wYA) |
## Blogs
## Libraies
# Awesome Optimization Methods
## Articles And Interpretation
| Type | 简称 | | | | | |
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| | | | | | | |
| | CNN | Trans | CNN&Trans | RNN | GEN | Other |
| 网络类型 | CNN | Transformer | CNN + Transformer | RNN | GAN/Diffusion | GNN... |
| | | | | | | |
| | | | | | | |
| | Sup | UnSup | Self | Semi | Meta | Other |
| 监督方式 | Supervised Learning | Unsupervised Learning | Self-Supervised Learning | Semi-Supervised Learning | Meta Learning | Weakly-Supervised... |
| | | | | | | |
| | | | | | | |
| | Loss | Data | - | - | - | Other |
| 任务类型 | Loss Function | Data Augmentation | - | - | - | ... |
| | | | | | | |
| | | | | | | |
| | MultiModal | | - | - | - | Other |
| 其他特性 | MultiModal Learning | | - | - | - | - |
| Paper | Publication | Type | 性能 | 代码 | 解读 |
| :- | :---| :---:| :---: | :---: | :---: |
|LMFLOSS: A Hybrid Loss For Imbalanced Medical Image Classification | | __`CNN`__ __`Loss`__ | - | | [LMFLOSS：用于解决不平衡医学图像分类的新型混合损失函数](https://mp.weixin.qq.com/s/G8hyI4F-WWpcPG4UYPbfdQ) |
## Blogs
## Libraies
# Awesome Face Recognition
## Articles And Interpretation
| Type | 简称 | | | | | |
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| | | | | | | |
| | CNN | Trans | CNN&Trans | RNN | GEN | Other |
| 网络类型 | CNN | Transformer | CNN + Transformer | RNN | GAN/Diffusion | GNN... |
| | | | | | | |
| | | | | | | |
| | Sup | UnSup | Self | Semi | FSL | ZSL |
| 监督方式 | Supervised Learning | Unsupervised Learning | Self-Supervised Learning | Semi-Supervised Learning | Few-shot Learning | Zero-shot Learning |
| | | | | | | |
| | | | | | | |
| | - | - | - | - | - | - |
| 任务类型 | - | - | -| - | -| -... |
| | | | | | | |
| | | | | | | |
| | - | - | - | - | - | - |
| 其他特性 | - | - | - | - | - | - |
| Paper | Publication | Type | 性能 | 代码 | 解读 |
| :- | :---| :---:| :---: | :---: | :---: |
|BoundaryFace: A mining framework with noise label self-correction for Face Recognition | ECCV 2022 | __`CNN`__ | |[GitHub](https://github.com/SWJTU-3DVision/BoundaryFace) | [人脸识别新方法 BoundaryFace：一种基于噪声标签自校正框架(附源码实现)](https://mp.weixin.qq.com/s/U0luh7njdp2e3AjKoNfZbQ) |
## Blogs
## Libraies