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Vision Transformer models process input images by dividing them into a spatially regular grid of equal-size patches.
Quad trees a data structure for retrieval on composite keys
Raphael A. Finkel and Jon Louis Bentley · 1974
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Operations on images using quad trees
Gregory M. Hunter and Kenneth Steiglitz · 1979
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Octree encoding: A new technique for the representation, manipulation and display of arbitrary 3-d objects by computer
Donald Meagher · 1980
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Quad tree structures for image compression applications
Tassos Markas and John H. Reif · 1992
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Acceleration of stochastic approximation by averaging
Boris Polyak and Anatoli B. Juditsky · 1992
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A parallel hashed oct-tree n-body algorithm
Michael S. Warren and John K. Salmon · 1993
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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 S. Bernstein, Alexander C. Berg, and Li Fei-Fei · 2014
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Fast r-cnn
Ross B. Girshick · 2015
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch · 2015
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Deep networks with stochastic depth
Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, and Kilian Q. Weinberger · 2016
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Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
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Torchvision: Pytorch’s computer vision library
TorchVision maintainers and contributors · 2016
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Octnet: Learning deep 3d representations at high resolutions
Gernot Riegler, Ali O. Ulusoy, and Andreas Geiger · 2016
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R. Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, and Dhruv Batra · 2016
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Octree generating networks: Efficient convolutional architectures for high-resolution 3d outputs
Maxim Tatarchenko, Alexey Dosovitskiy, and Thomas Brox · 2017
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Ashish Vaswani, Noam M. Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cissé, Yann Dauphin, and David Lopez-Paz · 2017
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Autoaugment: Learning augmentation policies from data
Ekin Dogus Cubuk, Barret Zoph, Dandelion Mané, Vijay Vasudevan, and Quoc V. Le · 2018
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Quadtree convolutional neural networks
Pradeep Kumar Jayaraman, Jianhan Mei, Jianfei Cai, and Jianmin Zheng · 2018
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Subword regularization: Improving neural network translation models with multiple subword candidates
Taku Kudo · 2018
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Shufflenet v2: Practical guidelines for efficient cnn architecture design
Ningning Ma, Xiangyu Zhang, Haitao Zheng, and Jian Sun · 2018
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Levit: a vision transformer in convnet’s clothing for faster inference
Benjamin Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Herv’e J’egou, and Matthijs Douze · 2021
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A quadtree image representation for computational pathology
Robert Jewsbury, Abhir Bhalerao, and Nasir M. Rajpoot · 2021
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Mask transfiner for high-quality instance segmentation
Lei Ke, Martin Danelljan, Xia Li, Yu-Wing Tai, Chi-Keung Tang, and Fisher Yu · 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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Adavit: Adaptive vision transformers for efficient image recognition
Lingchen Meng, Hengduo Li, Bor-Chun Chen, Shiyi Lan, Zuxuan Wu, Yu-Gang Jiang, and Ser Nam Lim · 2021
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Quadtree generating networks: Efficient hierarchical scene parsing with sparse convolutions
Kashyap Chitta, José Manuel Álvarez, and Martial Hebert · 2019
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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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PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Pytorch image models
Ross Wightman · 2019
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Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Young Joon Yoo · 2019
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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 · 2020
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Taming transformers for high-resolution image synthesis
Patrick Esser, Robin Rombach, and Björn Ommer · 2020
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Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
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Dynamicvit: Efficient vision transformers with dynamic token sparsification
Yongming Rao, Wenliang Zhao, Benlin Liu, Jiwen Lu, Jie Zhou, and Cho-Jui Hsieh · 2021
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Tokenlearner: Adaptive space-time tokenization for videos
Michael S. Ryoo, A. J. Piergiovanni, Anurag Arnab, Mostafa Dehghani, and Anelia Angelova · 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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Early convolutions help transformers see better
Tete Xiao, Mannat Singh, Eric Mintun, Trevor Darrell, Piotr Dollár, and Ross B. Girshick · 2021
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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
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A-vit: Adaptive tokens for efficient vision transformer
Hongxu Yin, Arash Vahdat, José Manuel Álvarez, Arun Mallya, Jan Kautz, and Pavlo Molchanov · 2021
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Token merging: Your vit but faster
Daniel Bolya, Cheng-Yang Fu, Xiaoliang Dai, Peizhao Zhang, Christoph Feichtenhofer, and Judy Hoffman · 2022
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Learning to merge tokens in vision transformers
Cédric Renggli, André Susano Pinto, Neil Houlsby, Basil Mustafa, Joan Puigcerver, and Carlos Riquelme · 2022
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Revisiting weakly supervised pre-training of visual perception models
Mannat Singh, Laura Gustafson, Aaron B. Adcock, Vinicius de Freitas Reis, Buğra Gedik, Raj Prateek Kosaraju, Dhruv Kumar Mahajan, Ross B. Girshick, Piotr Doll’ar, and Laurens van der Maaten · 2022
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Quadtree attention for vision transformers
Shitao Tang, Jiahui Zhang, Siyu Zhu, and Ping Tan · 2022
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Maxvit: Multi-axis vision transformer
Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Conrad Bovik, and Yinxiao Li · 2022
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