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Vision Transformer (ViT) has gained increasing attention in the computer vision community in recent years.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, et al · 2009
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, et al · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Sun Jian · 2016
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Deep networks with stochastic depth
Gao Huang, Yu Sun, and Zhuang Liu · 2016
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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross B. Girshick · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross B. Girshick, and Kaiming He andPiotr Dollár · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, et al · 2017
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Scene parsing through ade20k dataset
Bolei Zhou, Hang Zhao, Xavier Puig, et al · 2017
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Cascade r-cnn: Delving into high quality object detection
Zhaowei Cai and Nuno Vasconcelos · 2018
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Unified perceptual parsing for scene understanding
Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, and Jian Sun · 2018
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, et al · 2018
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MMDetection: Open mmlab detection toolbox and benchmark
Kai Chen, Jiaqi Wang, Jiangmiao Pang, et al · 2019
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Panoptic feature pyramid networks
Alexander Kirillov, Ross Girshick, Kaiming He, and Piotr Dollár · 2019
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Acceleration of stochastic approximation by averaging
Boris T Polyak and Anatoli B Juditsky · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
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Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, et al · 2019
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Mmsegmentation, an open source semantic segmentation toolbox, 2020
MMSegmentation Contributors · 2020
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Randaugment: Practical automated data augmentation with a reduced search space
Ekin D Cubuk, Barret Zoph, Jonathon Shlens, et al · 2020
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Random erasing data augmentation
Zhun Zhong, Liang Zheng, Guoliang Kang, et al · 2020
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Twins: Revisiting the design of spatial attention in vision transformers
Xiangxiang Chu, Zhi Tian, Yuqing Wang, Bo Zhang, Haibing Ren, Xiaolin Wei, Huaxia Xia, and Chunhua Shen · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, et al · 2021
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Transformer in transformer
Kai Han, An Xiao, Enhua Wu, et al · 2021
Cited alongside, same era.
Scaling up visual and vision-language representation learning with noisy text supervision
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, et al · 2021
Cited alongside, same era.
All tokens matter: Token labeling for training better vision transformers
Zi-Hang Jiang, Qibin Hou, Li Yuan, Daquan Zhou, Yujun Shi, Xiaojie Jin, Anran Wang, and Jiashi Feng · 2021
Cited alongside, same era.
Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, et al · 2021
Cited alongside, same era.
Pyramid vision transformer: A versatile backbone for dense prediction without convolutions
Inception transformer
Chenyang Si, Weihao Yu, Pan Zhou, Yichen Zhou, Xinchao Wang, and Shuicheng YAN · 2022
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Quadtree attention for vision transformers
Shitao Tang, Jiahui Zhang, Siyu Zhu, et al · 2022
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Maxvit: Multi-axis vision transformer
Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, and Yinxiao Li · 2022
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Vision transformer with deformable attention
Zhuofan Xia, Xuran Pan, Shiji Song, Li Erran Li, and Gao Huang · 2022
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Wave-vit: Unifying wavelet and transformers for visual representation learning
Ting Yao, Yingwei Pan, Yehao Li, Chong-Wah Ngo, and Tao Mei · 2022
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Volo: Vision outlooker for visual recognition
Li Yuan, Qibin Hou, Zihang Jiang, Jiashi Feng, and Shuicheng Yan · 2022
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Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, and Ling Shao · 2021
Cited alongside, same era.
Cvt: Introducing convolutions to vision transformers
Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan, and Lei Zhang · 2021
Cited alongside, same era.
Focal self-attention for local-global interactions in vision transformers
Jianwei Yang, Chunyuan Li, Pengchuan Zhang, Xiyang Dai, Bin Xiao, Lu Yuan, and Jianfeng Gao · 2021
Cited alongside, same era.
Multi-scale vision longformer: A new vision transformer for high-resolution image encoding
Pengchuan Zhang, Xiyang Dai, Jianwei Yang, Bin Xiao, Lu Yuan, Lei Zhang, and Jianfeng Gao · 2021
Cited alongside, same era.
Learned queries for efficient local attention
Moab Arar, Ariel Shamir, and Amit H. Bermano · 2022
Cited alongside, same era.
RegionViT: Regional-to-Local Attention for Vision Transformers
Chun-Fu (Richard) Chen, Rameswar Panda, and Quanfu Fan · 2022
Cited alongside, same era.
Davit: Dual attention vision transformers
Mingyu Ding, Bin Xiao, Noel Codella, et al · 2022
Cited alongside, same era.
Vsa: Learning varied-size window attention in vision transformers
Qiming Zhang, Yufei Xu, Jing Zhang, and Dacheng Tao · 2022
Later among the works it cites.
Conditional positional encodings for vision transformers
Xiangxiang Chu, Zhi Tian, Bo Zhang, Xinlong Wang, and Chunhua Shen · 2023
Closest in time.
Rethinking local perception in lightweight vision transformer, 2023
Qihang Fan, Huaibo Huang, Jiyang Guan, and Ran He · 2023
Closest in time.
Sucheng ren, xingyi yang, songhua liu, xinchao wang
SG-Former: Self guided Transformer with Evolving Token Reallocation · 2023
Closest in time.
Neighborhood attention transformer
Ali Hassani, Steven Walton, Jiachen Li, Shen Li, and Humphrey Shi · 2023
Closest in time.
Global context vision transformers
Ali Hatamizadeh, Hongxu Yin, Greg Heinrich, Jan Kautz, and Pavlo Molchanov · 2023
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Scale-aware modulation meet transformer
Weifeng Lin, Ziheng Wu, Jiayu Chen, Jun Huang, and Lianwen Jin · 2023
Closest in time.
Unified-io: A unified model for vision, language, and multi-modal tasks
Jiasen Lu, Christopher Clark, Rowan Zellers, Roozbeh Mottaghi, and Aniruddha Kembhavi · 2023
Closest in time.
Retentive network: A successor to Transformer for large language models
Yutao Sun, Li Dong, Shaohan Huang, Shuming Ma, Yuqing Xia, Jilong Xue, Jianyong Wang, and Furu Wei · 2023
Closest in time.
Internimage: Exploring large-scale vision foundation models with deformable convolutions
Wenhai Wang, Jifeng Dai, Zhe Chen, Zhenhang Huang, Zhiqi Li, Xizhou Zhu, Xiaowei Hu, Tong Lu, Lewei Lu, Hongsheng Li, et al · 2023
Closest in time.
mplug-2: A modularized multi-modal foundation model across text, image and video
Haiyang Xu, Qinghao Ye, Ming Yan, Yaya Shi, Jiabo Ye, Yuanhong Xu, Chenliang Li, Bin Bi, Qi Qian, Wei Wang, Guohai Xu, Ji Zhang, Songfang Huang, Fei Huang, and Jingren Zhou · 2023
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Moat: Alternating mobile convolution and attention brings strong vision models
Chenglin Yang, Siyuan Qiao, Qihang Yu, et al · 2023
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Dual vision transformer
Ting Yao, Yehao Li, Yingwei Pan, Yu Wang, Xiao-Ping Zhang, and Tao Mei · 2023
Closest in time.
Biformer: Vision transformer with bi-level routing attention
Lei Zhu, Xinjiang Wang, Zhanghan Ke, Wayne Zhang, and Rynson Lau · 2023
Closest in time.