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Video understanding relies on perceiving the global content and modeling its internal connections (e.g., causality, movement, and spatio-temporal correspondence).
Navier-stokes, fluid dynamics, and image and video inpainting
Marcelo Bertalmio, Andrea L Bertozzi, and Guillermo Sapiro · 2001
Earlier work this paper cites.
Hmdb: A large video database for human motion recognition
Hilde Kuehne, Hueihan Jhuang, E. Garrote, T. Poggio, and Thomas Serre · 2011
Earlier work this paper cites.
Hmdb: a large video database for human motion recognition
Hildegard Kuehne, Hueihan Jhuang, Estíbaliz Garrote, Tomaso Poggio, and Thomas Serre · 2011
Earlier work this paper cites.
Ucf101: A dataset of 101 human actions classes from videos in the wild
K. Soomro, A. Zamir, and M. Shah · 2012
Earlier work this paper cites.
Ucf101: A dataset of 101 human actions classes from videos in the wild
Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah · 2012
Earlier work this paper cites.
Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A Efros · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Layer normalization
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
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Deep networks with stochastic depth
Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, and Kilian Q Weinberger · 2016
Earlier work this paper cites.
Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch · 2016
Earlier work this paper cites.
Conditional image generation with pixelcnn decoders
Aäron van den Oord, Nal Kalchbrenner, Lasse Espeholt, K. Kavukcuoglu, Oriol Vinyals, and A. Graves · 2016
Earlier work this paper cites.
Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al · 2016
Earlier work this paper cites.
Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
Earlier work this paper cites.
Quo vadis, action recognition? a new model and the kinetics dataset
Joao Carreira and Andrew Zisserman · 2017
Earlier work this paper cites.
Self-supervised video representation learning with odd-one-out networks
Basura Fernando, Hakan Bilen, Efstratios Gavves, and Stephen Gould · 2017
Earlier work this paper cites.
Audio set: An ontology and human-labeled dataset for audio events
Jort F Gemmeke, Daniel PW Ellis, Dylan Freedman, Aren Jansen, Wade Lawrence, R Channing Moore, Manoj Plakal, and Marvin Ritter · 2017
Earlier work this paper cites.
The "something something" video database for learning and evaluating visual common sense, 2017
Raghav Goyal, Samira Ebrahimi Kahou, Vincent Michalski, Joanna Materzyńska, Susanne Westphal, Heuna Kim, Valentin Haenel, Ingo Fruend, Peter Yianilos, Moritz Mueller-Freitag, Florian Hoppe, Christian Thurau, Ingo Bax, and Roland Memisevic · 2017
Earlier work this paper cites.
Unsupervised representation learning by sorting sequences
Hsin-Ying Lee, Jia-Bin Huang, Maneesh Singh, and Ming-Hsuan Yang · 2017
Earlier work this paper cites.
Using the output embedding to improve language models
Ofir Press and Lior Wolf · 2017
Earlier work this paper cites.
Revisiting unreasonable effectiveness of data in deep learning era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta · 2017
Earlier work this paper cites.
Neural discrete representation learning
Aäron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
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Resound: Towards action recognition without representation bias
Yingwei Li, Yi Li, and Nuno Vasconcelos · 2018
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2018
Cited alongside, same era.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Cited alongside, same era.
A closer look at spatiotemporal convolutions for action recognition
Du Tran, Heng Wang, Lorenzo Torresani, Jamie Ray, Yann LeCun, and Manohar Paluri · 2018
Cited alongside, same era.
Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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Generative pretraining from pixels
Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, and Ilya Sutskever · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2020
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Randaugment: Practical automated data augmentation with a reduced search space
Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le · 2020
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Donglai Wei, Joseph J Lim, Andrew Zisserman, and William T Freeman · 2018
Cited alongside, same era.
Generative image inpainting with contextual attention
Jiahui Yu, Zhe Lin, Jimei Yang, Xiaohui Shen, Xin Lu, and Thomas S Huang · 2018
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2018
Cited alongside, same era.
A short note on the kinetics-700 human action dataset
Joao Carreira, Eric Noland, Chloe Hillier, and Andrew Zisserman · 2019
Cited alongside, same era.
Generating long sequences with sparse transformers
Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever · 2019
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Cited alongside, same era.
Slowfast networks for video recognition
Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He · 2019
Cited alongside, same era.
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Oops! predicting unintentional action in video
Dave Epstein, Boyuan Chen, and Carl Vondrick · 2020
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Bootstrap your own latent: A new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, et al · 2020
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Bootstrap your own latent a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, et al · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Spanbert: Improving pre-training by representing and predicting spans
Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S Weld, Luke Zettlemoyer, and Omer Levy · 2020
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Late temporal modeling in 3d cnn architectures with bert for action recognition
M Esat Kalfaoglu, Sinan Kalkan, and A Aydin Alatan · 2020
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End-to-end learning of visual representations from uncurated instructional videos
Antoine Miech, Jean-Baptiste Alayrac, Lucas Smaira, Ivan Laptev, Josef Sivic, and Andrew Zisserman · 2020
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What makes for good views for contrastive learning
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola · 2020
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Vivit: A video vision transformer
Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, and Cordelia Schmid · 2021
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Is space-time attention all you need for video understanding?
Gedas Bertasius, Heng Wang, and Lorenzo Torresani · 2021
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An empirical study of training self-supervised visual transformers
Xinlei Chen, Saining Xie, and Kaiming He · 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, et al · 2021
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A large-scale study on unsupervised spatiotemporal representation learning
Christoph Feichtenhofer, Haoqi Fan, Bo Xiong, Ross Girshick, and Kaiming He · 2021
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Movinets: Mobile video networks for efficient video recognition
Dan Kondratyuk, Liangzhe Yuan, Yandong Li, Li Zhang, Mingxing Tan, Matthew Brown, and Boqing Gong · 2021
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Spatiotemporal contrastive video representation learning
Rui Qian, Tianjian Meng, Boqing Gong, Ming-Hsuan Yang, Huisheng Wang, Serge Belongie, and Yin Cui · 2021
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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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Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
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Jacob Walker, Ali Razavi, and Aäron van den Oord · 2021
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Videogpt: Video generation using vq-vae and transformers
Wilson Yan, Yunzhi Zhang, Pieter Abbeel, and Aravind Srinivas · 2021
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