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Vision Transformers convert images to sequences by slicing them into patches.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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Learning Multiple Layers of Features from Tiny Images
Alex Krizhevsky · 2009
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Sun database: Large-scale scene recognition from abbey to zoo
Jianxiong Xiao, James Hays, Krista A Ehinger, Aude Oliva, and Antonio Torralba · 2010
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Algorithms for learning kernels based on centered alignment
Corinna Cortes, Mehryar Mohri, and Afshin Rostamizadeh · 2012
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Cats and dogs
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar · 2012
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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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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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Microsoft COCO captions: Data collection and evaluation server
Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollár, and C. Lawrence Zitnick · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 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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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Lei Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
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The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
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Visual genome: Connecting language and vision using crowdsourced dense image annotations
Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A Shamma, et al · 2017
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Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 2018
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Once for all: Train one network and specialize it for efficient deployment
Han Cai, Chuang Gan, and Song Han · 2019
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On the efficacy of knowledge distillation
Jang Hyun Cho and Bharath Hariharan · 2019
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LVIS: A dataset for large vocabulary instance segmentation
Agrim Gupta, Piotr Dollar, and Ross Girshick · 2019
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Panoptic segmentation
Alexander Kirillov, Kaiming He, Ross Girshick, Carsten Rother, and Piotr Dollár · 2019
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Large scale learning of general visual representations for transfer
Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, and Neil Houlsby · 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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Do imagenet classifiers generalize to imagenet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2019
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Objects365: A Large-Scale, High-Quality Dataset for Object Detection
Shuai Shao, Zeming Li, Tianyuan Zhang, Chao Peng, Gang Yu, Xiangyu Zhang, Jing Li, and Jian Sun · 2019
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A multigrid method for efficiently training video models
Chao-Yuan Wu, Ross B. Girshick, Kaiming He, Christoph Feichtenhofer, and Philipp Krähenbühl · 2019
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Semantic understanding of scenes through the ADE20K dataset
Bolei Zhou, Hang Zhao, Xavier Puig, Tete Xiao, Sanja Fidler, Adela Barriuso, and Antonio Torralba · 2019
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Lucas Beyer, Olivier J. Hénaff, Alexander Kolesnikov, Xiaohua Zhai, and Aäron van den Oord · 2020
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Reducing transformer depth on demand with structured dropout
Angela Fan, Edouard Grave, and Armand Joulin · 2020
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Self-training with noisy student improves imagenet classification
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V Le · 2020
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Going deeper with image transformers
Hugo Touvron, Matthieu Cord, Alexandre Sablayrolles, Gabriel Synnaeve, and Hervé Jégou · 2021
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Not all images are worth 16x16 words: Dynamic transformers for efficient image recognition
Yulin Wang, Rui Huang, Shiji Song, Zeyi Huang, and Gao Huang · 2021
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Generalized shape metrics on neural representations
Alex H Williams, Erin Kunz, Simon Kornblith, and Scott Linderman · 2021
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Better plain vit baselines for imagenet-1k
Lucas Beyer, Xiaohua Zhai, and Alexander Kolesnikov · 2022
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Knowledge distillation: A good teacher is patient and consistent
Lucas Beyer, Xiaohua Zhai, Amélie Royer, Larisa Markeeva, Rohan Anil, and Alexander Kolesnikov · 2022
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Auto-scaling vision transformers without training
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Bignas: Scaling up neural architecture search with big single-stage models
Jiahui Yu, Pengchong Jin, Hanxiao Liu, Gabriel Bender, Pieter-Jan Kindermans, Mingxing Tan, Thomas S. Huang, Xiaodan Song, Ruoming Pang, and Quoc Le · 2020
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VATT: transformers for multimodal self-supervised learning from raw video, audio and text
Hassan Akbari, Liangzhe Yuan, Rui Qian, Wei-Hong Chuang, Shih-Fu Chang, Yin Cui, and Boqing Gong · 2021
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Understanding robustness of transformers for image classification
Srinadh Bhojanapalli, Ayan Chakrabarti, Daniel Glasner, Daliang Li, Thomas Unterthiner, and Andreas Veit · 2021
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Transformer interpretability beyond attention visualization
Hila Chefer, Shir Gur, and Lior Wolf · 2021
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Mostafa Dehghani, Anurag Arnab, Lucas Beyer, Ashish Vaswani, and Yi Tay · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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The many faces of robustness: A critical analysis of out-of-distribution generalization
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, and Justin Gilmer · 2021
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Wuyang Chen, Wei Huang, Xianzhi Du, Xiaodan Song, Zhangyang Wang, and Denny Zhou · 2022
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Nasvit: Neural architecture search for efficient vision transformers with gradient conflict aware supernet training
Chengyue Gong, Dilin Wang, Meng Li, Xinlei Chen, Zhicheng Yan, Yuandong Tian, Qiang Liu, and Vikas Chandra · 2022
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross B. Girshick · 2022
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Uvim: A unified modeling approach for vision with learned guiding codes
Alexander Kolesnikov, André Susano Pinto, Lucas Beyer, Xiaohua Zhai, Jeremiah Harmsen, and Neil Houlsby · 2022
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Matryoshka representations for adaptive deployment
Aditya Kusupati, Gantavya Bhatt, Aniket Rege, Matthew Wallingford, Aditya Sinha, Vivek Ramanujan, William Howard-Snyder, Kaifeng Chen, Sham M. Kakade, Prateek Jain, and Ali Farhadi · 2022
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ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models
Chunyuan Li, Haotian Liu, Liunian Harold Li, Pengchuan Zhang, Jyoti Aneja, Jianwei Yang, Ping Jin, Houdong Hu, Zicheng Liu, Yong Jae Lee, and Jianfeng Gao · 2022
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Mingbao Lin, Mengzhao Chen, Yuxin Zhang, Ke Li, Yunhang Shen, Chunhua Shen, and Rongrong Ji · 2022
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A convnet for the 2020s
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie · 2022
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Simple open-vocabulary object detection with vision transformers
Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby · 2022
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Multimodal contrastive learning with limoe: the language-image mixture of experts
Basil Mustafa, Carlos Riquelme, Joan Puigcerver, Rodolphe Jenatton, and Neil Houlsby · 2022
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How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
Andreas Peter Steiner, Alexander Kolesnikov, Xiaohua Zhai, Ross Wightman, Jakob Uszkoreit, and Lucas Beyer · 2022
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Patch slimming for efficient vision transformers
Yehui Tang, Kai Han, Yunhe Wang, Chang Xu, Jianyuan Guo, Chao Xu, and Dacheng Tao · 2022
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Hugo Touvron, Matthieu Cord, and Hervé Jégou · 2022
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A-vit: Adaptive tokens for efficient vision transformer
Hongxu Yin, Arash Vahdat, Jose M. Alvarez, Arun Mallya, Jan Kautz, and Pavlo Molchanov · 2022
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Width & depth pruning for vision transformers
Fang Yu, Kun Huang, Meng Wang, Yuan Cheng, Wei Chu, and Li Cui · 2022
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MERLOT RESERVE: neural script knowledge through vision and language and sound
Rowan Zellers, Jiasen Lu, Ximing Lu, Youngjae Yu, Yanpeng Zhao, Mohammadreza Salehi, Aditya Kusupati, Jack Hessel, Ali Farhadi, and Yejin Choi · 2022
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Scaling vision transformers
Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer · 2022
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Lit: Zero-shot transfer with locked-image text tuning
Xiaohua Zhai, Xiao Wang, Basil Mustafa, Andreas Steiner, Daniel Keysers, Alexander Kolesnikov, and Lucas Beyer · 2022
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