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Awesome Transformers (self-attention) in Computer Vision

• Attention Is All You Need, NeurIPS 2017
  • Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin
  • [paper] [official code] [pytorch implementation]
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, NAACL 2019
  • Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova
  • [paper] [offficial code] [huggingface/transformers]
• Efficient Transformers: A Survey, arXiv 2020
  • Yi Tay, Mostafa Dehghani, Dara Bahri, Donald Metzler
  • [paper]

• Attention augmented convolutional networks, ICCV 2019, image classification
  • Irwan Bello, Barret Zoph, Ashish Vaswani, Jonathon Shlens, Quoc V. Le
  • [paper] [pytorch implementation]
• Self-Attention Generative Adversarial Networks, ICML 2019, generative model(GANs)
  • Han Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odena
  • [paper] [official code]
• Videobert: A joint model for video and language representation learning, ICCV 2019, video processing
  • Chen Sun, Austin Myers, Carl Vondrick, Kevin Murphy, Cordelia Schmid
  • [paper]
• Visual Transformers: Token-based Image Representation and Processing for Computer Vision, arXiv 2020, image classification
  • Bichen Wu, Chenfeng Xu, Xiaoliang Dai, Alvin Wan, Peizhao Zhang, Masayoshi Tomizuka, Kurt Keutzer, Peter Vajda
  • [paper]
• Feature Pyramid Transformer, ECCV 2020, detection and segmentation
  • Dong Zhang, Hanwang Zhang, Jinhui Tang, Meng Wang, Xiansheng Hua, Qianru Sun
  • [paper] [official code]
• Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers, arXiv 2020, depth estimation
  • Zhaoshuo Li, Xingtong Liu, Francis X. Creighton, Russell H. Taylor, and Mathias Unberath
  • [paper] [official code]
• End-to-end Lane Shape Prediction with Transformers, arXiv 2020, lane detection
  • Ruijin Liu, Zejian Yuan, Tie Liu, Zhiliang Xiong
  • [paper] [official code]

• End-to-end object detection with transformers, ECCV 2020, object detection
  • Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko
  • [paper] [official code] [detectron2 implementation]
• Deformable DETR: Deformable Transformers for End-to-End Object Detection, arXiv 2020, object detection
  • Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, Jifeng Dai
  • [paper] [official code]
• End-to-End Object Detection with Adaptive Clustering Transformer, arXiv 2020, object detection
  • Minghang Zheng, Peng Gao, Xiaogang Wang, Hongsheng Li, Hao Dong
  • [paper]
• UP-DETR: Unsupervised Pre-training for Object Detection with Transformers, arXiv 2020, object detection
  • Zhigang Dai, Bolun Cai, Yugeng Lin, Junying Chen
  • [paper]
• DETR for Pedestrian Detection, arXiv 2020, pedestrian detection
  • Matthieu Lin, Chuming Li, Xingyuan Bu, Ming Sun, Chen Lin, Junjie Yan, Wanli Ouyang, Zhidong Deng
  • [paper]

• Image Transformer, ICML 2018
  • Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Łukasz Kaiser, Noam Shazeer, Alexander Ku, Dustin Tran
  • [paper] [official code]
• Stand-alone self-attention in vision models, NeurIPS 2019
  • Prajit Ramachandran, Niki Parmar, Ashish Vaswani, Irwan Bello, Anselm Levskaya, Jonathon Shlens
  • [paper] [official code(underconstruction)]
• On the relationship between self-attention and convolutional layers, ICLR 2020
  • Jean-Baptiste Cordonnier, Andreas Loukas, Martin Jaggi
  • [paper] [official code]
• Exploring self-attention for image recognition, CVPR 2020
  • Hengshuang Zhao, Jiaya Jia, Vladlen Koltun
  • [paper] [official code]

• Generating long sequences with sparse transformers, arXiv 2019
  • Rewon Child, Scott Gray, Alec Radford, Ilya Sutskever
  • [paper] [official code]
• Scaling autoregressive video models, ICLR 2019
  • Dirk Weissenborn, Oscar Täckström, Jakob Uszkoreit
  • [paper]
• Axial attention in multidimensional transformers, arXiv 2019
  • Jonathan Ho, Nal Kalchbrenner, Dirk Weissenborn, Tim Salimans
  • [paper] [pytorch implementation]
• Axial-deeplab: Stand-alone axial-attention for panoptic segmentation, ECCV 2020
  • Huiyu Wang, Yukun Zhu, Bradley Green, Hartwig Adam, Alan Yuille, Liang-Chieh Chen
  • [paper] [pytorch implementation]

• Generative pretraining from pixels, ICML 2020, iGPT
  • Mark Chen, Alec Radford, Rewon Child, Jeff Wu, Heewoo Jun, Prafulla Dhariwal, David Luan, Ilya Sutskever
  • [paper] [official code]
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, arXiv 2020, ViT
  • Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby
  • [paper] [pytorch implementation]
• Pre-Trained Image Processing Transformer, arXiv, IPT
  • Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, Wen Gao
  • [paper]

• Point Transformer, arXiv 2020, points classification + part/semantic segmentation
  • Hengshuang Zhao, Li Jiang, Jiaya Jia, Philip Torr, Vladlen Koltun
  • [paper]

• ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks, NeurIPS 2019
  • Jiasen Lu, Dhruv Batra, Devi Parikh, Stefan Lee
  • [paper] [official code]
• LXMERT: Learning Cross-Modality Encoder Representations from Transformers, EMNLP 2019
  • Hao Tan, Mohit Bansal
  • [paper] [official code]
• VisualBERT: A Simple and Performant Baseline for Vision and Language, arXiv 2019
  • Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang
  • [paper] [official code]
• VL-BERT: Pre-training of Generic Visual-Linguistic Representations, ICLR 2020
  • Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, Jifeng Dai
  • [paper] [official code]
• UNITER: UNiversal Image-TExt Representation Learning, ECCV 2020
  • Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, Jingjing Liu
  • [paper] [official code]

• Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training, AAAI 2020
  • Gen Li, Nan Duan, Yuejian Fang, Ming Gong, Daxin Jiang, Ming Zhou
  • [paper] [official code]
• ImageBERT: Cross-modal Pre-training with Large-scale Weak-supervised Image-Text Data, arXiv 2020
  • Di Qi, Lin Su, Jia Song, Edward Cui, Taroon Bharti, Arun Sacheti
  • [paper]
• Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks, ECCV 2020
  • Xiujun Li, Xi Yin, Chunyuan Li, Pengchuan Zhang, Xiaowei Hu, Lei Zhang, Lijuan Wang, Houdong Hu, Li Dong, Furu Wei, Yejin Choi, Jianfeng Gao
  • [paper] [official code]

• LayoutLM: Pre-training of Text and Layout for Document Image Understanding
  • Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou
  • [paper] [official code]

• 12-in-1: Multi-Task Vision and Language Representation Learning
  • Jiasen Lu, Vedanuj Goswami, Marcus Rohrbach, Devi Parikh, Stefan Lee
  • [paper] [official code]