KO
|
EN
gitlite — search
Search
#python
#java
#python3
#arduino
#golang
#machine-learning
#rust
#html
#flask
#javascript
#seismology
#nodejs
VisionTransformer
★ 11
Open GitHub ↗
No description available.
Download README (.md)
Explore Similar Repositories
symfony-website-docker
:
Create your Symfony website project (Or use a existing one !) with Caddy, PHP-FPM, MariaDB and phpMyAdmin with only one command with docker-compose ! :tada: :man_technologist: :woman_technologist:
voodoo
:
run UEFI apps
CoinAnimation
:
A sample project to demonstrate power of Android animations
create-girl-friend
:
Create your girl friend. 创建你的对象（女朋友）。
backend
:
Backend of Pangolin Desktop
// repository documentation
Was this content helpful?
★ 0
(0 ratings)
Select Rating:
★
★
★
★
★
Submit Feedback
Recent Feedback
×
Download README
Do you want to download the
README.md
file for
VisionTransformer
?
Download (.md)
# Awesome Transformer in Vision [](https://awesome.re) A curated list of vision transformer related resources. Please feel free to [pull requests](https://github.com/penghouwen/VisionTransformer/pulls) or [open an issue](https://github.com/penghouwen/VisionTransformer/issues) to add papers. ## Table of Contents - [Awesome Surveys](#awesome-surveys) - [Transformer in Vision](#transformer-in-vision) - [2021 Venues](#2021) - [2020 Venues](#2020) - [2019 Venues](#2019) - [Previous Venues](#2012-2018) - [Awesome Libraies](#awesome-surveys) ## Awesome Surveys | Title | Venue | BibTeX | |:--------|:--------:|:--------:| | [A Survey on Visual Transformer](https://arxiv.org/pdf/2012.12556.pdf) | ArXiv | [Bib](https://scholar.googleusercontent.com/scholar.bib?q=info:Aj10Crv7DScJ:scholar.google.com/&output=citation&scisdr=CgUmooQTEM3KnAOogfQ:AAGBfm0AAAAAX_-tmfT1yhaAeO62lS61HGcSpcXSUqJ5&scisig=AAGBfm0AAAAAX_-tmQAIcm-VKBRqnb9iTs8Sghq-6ssB&scisf=4&ct=citation&cd=-1&hl=ja) ## Transformer in Vision | Task | Reg | Det | Seg | Trk | Other | |:------------|:--------------:|:----------------------:|:-----------------------:|:----------------------:|:----------:| | Explanation | Image Recoginition | Object Detection | Image Segmentation | Object Tracking | other types | You can add a tag for `domains` which contains several transformer-based works ### 2021 (Pls follow Time Inverse Ranking) | Title | Venue | Task | Code | BibTeX | |:--------|:--------:|:--------:|:--------:|:--------:| | **[T2T-ViT]**[Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet](https://arxiv.org/abs/2101.11986) | Arxiv | Reg | [GitHub](https://github.com/yitu-opensource/T2T-ViT) | <details> <summary>Bib</summary> <p align="left"> </br> @article{yuan2021tokens, </br> title={Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet}, </br> author={Yuan, Li and Chen, Yunpeng and Wang, Tao and Yu, Weihao and Shi, Yujun and Tay, Francis EH and Feng, Jiashi and Yan, Shuicheng}, </br> journal={arXiv preprint arXiv:2101.11986}, </br> year={2021} </br> } </p></details> </br> | **[BoTNet]**[Bottleneck Transformers for Visual Recognition](https://arxiv.org/abs/2101.11605) | Arxiv | Reg | [GitHub](https://gist.github.com/aravindsrinivas/56359b79f0ce4449bcb04ab4b56a57a2) | <details> <summary>Bib</summary> <p align="left"> </br> @article{srinivas2021bottleneck, </br> title={Bottleneck Transformers for Visual Recognition}, </br> author={Srinivas, Aravind and Lin, Tsung-Yi and Parmar, Niki and Shlens, Jonathon and Abbeel, Pieter and Vaswani, Ashish}, </br> journal={arXiv preprint arXiv:2101.11605}, </br> year={2021} </br> } </p></details> </br> | **[SSTVOS]**[SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation](https://arxiv.org/abs/2101.08833) | Arxiv | Seg | --- | <details> <summary>Bib</summary> <p align="left"> </br> @article{duke2021sstvos, </br> title={SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation}, </br> author={Duke, Brendan and Ahmed, Abdalla and Wolf, Christian and Aarabi, Parham and Taylor, Graham W}, </br> journal={arXiv preprint arXiv:2101.08833}, </br> year={2021} </br> } </p></details> </br> | **[TrackFormer]**[TrackFormer: Multi-Object Tracking with Transformers](https://arxiv.org/abs/2101.02702) | Arxiv | Trk | --- | <details> <summary>Bib</summary> <p align="left"> </br> @article{meinhardt2021trackformer, </br> title={TrackFormer: Multi-Object Tracking with Transformers}, </br> author={Meinhardt, Tim and Kirillov, Alexander and Leal-Taixe, Laura and Feichtenhofer, Christoph}, </br> journal={arXiv preprint arXiv:2101.02702}, </br> year={2021} </br> } </p></details> </br> ### 2020 | Title | Venue | Task | Code | BibTeX | |:--------|:--------:|:--------:|:--------:|:--------:| | **[DeiT]**[Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) | ArXiv | Reg | [GitHub](https://github.com/facebookresearch/deit) | <details> <summary>Bib</summary> <p align="left"> </br> @article{touvron2020training, </br> title={Training data-efficient image transformers \& distillation through attention}, </br> author={Touvron, Hugo and Cord, Matthieu and Douze, Matthijs and Massa, Francisco and Sablayrolles, Alexandre and J{\'e}gou, Herv{\'e}}, </br> journal={arXiv preprint arXiv:2012.12877}, </br> year={2020} </br> } </br> </p></details> </br> | **[ViT]**[An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) | ICLR | Reg | [GitHub](https://github.com/google-research/vision_transformer) | <details> <summary>Bib</summary> <p align="left"> </br> @article{dosovitskiy2020image, </br> title={An image is worth 16x16 words: Transformers for image recognition at scale}, </br> author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and others}, </br> journal={arXiv preprint arXiv:2010.11929}, </br> year={2020} </br> } </p></details> </br> | **[ViT-FRCNN]**[ Toward Transformer-Based Object Detection](https://arxiv.org/abs/2012.09958) | ArXiv | Det | --- | <details> <summary>Bib</summary> <p align="left"> </br> @article{beal2020toward, </br> title={Toward Transformer-Based Object Detection}, </br> author={Beal, Josh and Kim, Eric and Tzeng, Eric and Park, Dong Huk and Zhai, Andrew and Kislyuk, Dmitry}, </br> journal={arXiv preprint arXiv:2012.09958}, </br> year={2020} </br> } </p></details> </br> | **[TSP-FOCS]**[ Rethinking Transformer-based Set Prediction for Object Detection](https://arxiv.org/abs/2011.10881) | ArXiv | Det | --- | <details> <summary>Bib</summary> <p align="left"> </br> @article{sun2020rethinking, </br> title={Rethinking Transformer-based Set Prediction for Object Detection}, </br> author={Sun, Zhiqing and Cao, Shengcao and Yang, Yiming and Kitani, Kris}, </br> journal={arXiv preprint arXiv:2011.10881}, </br> year={2020} </br> } </p></details> </br> | **[UP-DETR]**[ UP-DETR: Unsupervised Pre-training for Object Detection with Transformers](https://arxiv.org/abs/2011.09094) | ArXiv | Det | --- | <details> <summary>Bib</summary> <p align="left"> </br> @article{dai2020up, </br> title={UP-DETR: Unsupervised Pre-training for Object Detection with Transformers}, </br> author={Dai, Zhigang and Cai, Bolun and Lin, Yugeng and Chen, Junying}, </br> journal={arXiv preprint arXiv:2011.09094}, </br> year={2020} </br> } </p></details> </br> | **[Deformable DETR]**[ Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/abs/2010.04159) | ArXiv | Det | [ GitHub]( https://github.com/fundamentalvision/Deformable-DETR) | <details> <summary>Bib</summary> <p align="left"> </br> @article{zhu2020deformable, </br> title={Deformable DETR: Deformable Transformers for End-to-End Object Detection}, </br> author={Zhu, Xizhou and Su, Weijie and Lu, Lewei and Li, Bin and Wang, Xiaogang and Dai, Jifeng}, </br> journal={arXiv preprint arXiv:2010.04159}, </br> year={2020} </br> } </p></details> </br> | **[DETR]**[ End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) | ECCV | Det | [ GitHub]( https://github.com/facebookresearch/detr) | <details> <summary>Bib</summary> <p align="left"> article{zhu2020deformable, </br> title={Deformable DETR: Deformable Transformers for End-to-End Object Detection}, </br> author={Zhu, Xizhou and Su, Weijie and Lu, Lewei and Li, Bin and Wang, Xiaogang and Dai, Jifeng}, </br> journal={arXiv preprint arXiv:2010.04159}, </br> year={2020} </br> } </br> </p></details> | **[SETR]**[Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://arxiv.org/abs/2012.15840) | Arxiv | Seg | [Github](https://github.com/fudan-zvg/SETR) | <details> <summary>Bib</summary> <p align="left"> @article{zheng2020rethinking, </br> title={Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers}, </br> author={Zheng, Sixiao and Lu, Jiachen and Zhao, Hengshuang and Zhu, Xiatian and Luo, Zekun and Wang, Yabiao and Fu, Yanwei and Feng, Jianfeng and Xiang, Tao and Torr, Philip HS and others}, </br> journal={arXiv preprint arXiv:2012.15840}, </br> year={2020} </br> } </br> </p></details> | **[MaX-DeepLab]**[MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers](https://arxiv.org/abs/2012.00759) | Arxiv | Seg | --- | <details> <summary>Bib</summary> <p align="left"> @article{wang2020max, </br> title={MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers}, </br> author={Wang, Huiyu and Zhu, Yukun and Adam, Hartwig and Yuille, Alan and Chen, Liang-Chieh}, </br> journal={arXiv preprint arXiv:2012.00759}, </br> year={2020} </br> } </br> </p></details> | **[TransTrack]**[TransTrack: Multiple-Object Tracking with Transformer](https://arxiv.org/abs/2012.15460) | ArXiv | Trk | [GitHub](https://github.com/PeizeSun/TransTrack) | <details> <summary>Bib</summary> <p align="left"> </br> @article{sun2020transtrack, </br> title={TransTrack: Multiple-Object Tracking with Transformer}, </br> author={Sun, Peize and Jiang, Yi and Zhang, Rufeng and Xie, Enze and Cao, Jinkun and Hu, Xinting and Kong, Tao and Yuan, Zehuan and Wang, Changhu and Luo, Ping}, </br> journal={arXiv preprint arXiv:2012.15460}, </br> year={2020} </br> } </p></details> </br> ### 2019 | Title | Venue | Task | Code | BibTeX | |:--------|:--------:|:--------:|:--------:|:--------:| | **[SASA]** [Stand-Alone Self-Attention in Vision Models](https://arxiv.org/pdf/1906.05909.pdf) | ArXiv | Reg | - | - ### 2012-2018 | Title | Venue | Task | Code | BibTeX | |:--------|:--------:|:--------:|:--------:|:--------:| | [Attention Is All You Need](https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf) | NeurIPS'17 | -- | [GitHub](https://github.com/tensorflow/tensor2tensor) | <details> <summary>Bib</summary> <p align="left"> @inproceedings{vaswani2017attention, </br> title={Attention is all you need}, </br> author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia}, </br> booktitle={Advances in neural information processing systems}, </br> pages={5998--6008}, </br> year={2017} </br> } </p></details> ## Awesome vTransformer Libraies - [WaitingToAdd](https://github.com/penghouwen/VisionTransformer/blob/main/README.md)
