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Vision Transformer (ViT) extracts the final representation from either class token or an average of all patch tokens, following the architecture of Transformer in Natural Language Processing (NLP) or Convolutional Neural Networks (CNNs) in computer vision.
Gradient-based learning applied to document recognition
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Integral channel features
Piotr Dollár, Zhuowen Tu, Pietro Perona, and Serge Belongie · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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The Caltech-UCSD Birds-200-2011 Dataset
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
Johannes Stallkamp, Marc Schlipsing, Jan Salmen, and Christian Igel · 2012
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Challenges in representation learning: A report on three machine learning contests
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Collecting a large-scale dataset of fine-grained cars
Jonathan Krause, Jia Deng, Michael Stark, and Fei-Fei Li · 2013
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3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
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Food-101–mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
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Describing textures in the wild
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, , and A. Vedaldi · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Aggregating deep convolutional features for image retrieval
Artem Babenko and Victor Lempitsky · 2015
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Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models
Bryan A Plummer, Liwei Wang, Chris M Cervantes, Juan C Caicedo, Julia Hockenmaier, and Svetlana Lazebnik · 2015
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Cross-dimensional weighting for aggregated deep convolutional features
Yannis Kalantidis, Clayton Mellina, and Simon Osindero · 2016
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Deep metric learning via lifted structured feature embedding
Hyun Oh Song, Yu Xiang, Stefanie Jegelka, and Silvio Savarese · 2016
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Visual instance retrieval with deep convolutional networks
Ali S Razavian, Josephine Sullivan, Stefan Carlsson, and Atsuto Maki · 2016
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Particular object retrieval with integral max-pooling of cnn activations
Giorgos Tolias, Ronan Sicre, and Hervé Jégou · 2016
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Remote sensing image scene classification: Benchmark and state of the art
Gong Cheng, Junwei Han, and Xiaoqiang Lu · 2017
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Xception: Deep learning with depthwise separable convolutions
François Chollet · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
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Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens Van Der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick · 2017
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Ms-rmac: Multiscale regional maximum activation of convolutions for image retrieval
Yang Li, Yulong Xu, Jiabao Wang, Zhuang Miao, and Yafei Zhang · 2017
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Normface: L2 hypersphere embedding for face verification
Feng Wang, Xiang Xiang, Jian Cheng, and Alan Loddon Yuille · 2017
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
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Fine-tuning cnn image retrieval with no human annotation
Filip Radenović, Giorgos Tolias, and Ondřej Chum · 2018
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Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning
Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut · 2018
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Additive margin softmax for face verification
Feng Wang, Jian Cheng, Weiyang Liu, and Haijun Liu · 2018
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Weighted generalized mean pooling for deep image retrieval
Xiaomeng Wu, Go Irie, Kaoru Hiramatsu, and Kunio Kashino · 2018
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Group normalization
Yuxin Wu and Kaiming He · 2018
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Training vision transformers for image retrieval
Alaaeldin El-Nouby, Natalia Neverova, Ivan Laptev, and Hervé Jégou · 2021
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You only look at one sequence: Rethinking transformer in vision through object detection
Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, and Wenyu Liu · 2021
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Simcse: Simple contrastive learning of sentence embeddings
Tianyu Gao, Xingcheng Yao, and Danqi Chen · 2021
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2021
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Rethinking spatial dimensions of vision transformers
Byeongho Heo, Sangdoo Yun, Dongyoon Han, Sanghyuk Chun, Junsuk Choe, and Seong Joon Oh · 2021
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth · 2019
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Combination of multiple global descriptors for image retrieval
HeeJae Jun, Byungsoo Ko, Youngjoon Kim, Insik Kim, and Jongtack Kim · 2019
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Similarity of neural network representations revisited
Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey Hinton · 2019
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Are sixteen heads really better than one?
Paul Michel, Omer Levy, and Graham Neubig · 2019
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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych · 2019
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Scaling up visual and vision-language representation learning with noisy text supervision
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig · 2021
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Vilt: Vision-and-language transformer without convolution or region supervision
Wonjae Kim, Bokyung Son, and Ildoo Kim · 2021
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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
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Slip: Self-supervision meets language-image pre-training
Norman Mu, Alexander Kirillov, David Wagner, and Saining Xie · 2021
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Scalable vision transformers with hierarchical pooling
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Do vision transformers see like convolutional neural networks?
Maithra Raghu, Thomas Unterthiner, Simon Kornblith, Chiyuan Zhang, and Alexey Dosovitskiy · 2021
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Segmenter: Transformer for semantic segmentation
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
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Pyramid vision transformer: A versatile backbone for dense prediction without convolutions
Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, and Ling Shao · 2021
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Masked feature prediction for self-supervised visual pre-training
Chen Wei, Haoqi Fan, Saining Xie, Chao-Yuan Wu, Alan Yuille, and Christoph Feichtenhofer · 2021
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ibot: Image bert pre-training with online tokenizer
Jinghao Zhou, Chen Wei, Huiyu Wang, Wei Shen, Cihang Xie, Alan Yuille, and Tao Kong · 2021
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Data2vec: A general framework for self-supervised learning in speech, vision and language
Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, and Michael Auli · 2022
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Beit: Bert pre-training of image transformers
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Joint global and local hierarchical priors for learned image compression
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Exploring plain vision transformer backbones for object detection
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How do vision transformers work?
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