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Effective modeling of complex spatiotemporal dependencies in long-form videos remains an open problem.
A method of analysing the behaviour of linear systems in terms of time series
Arnold Tustin · 1947
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Sequential deep learning for human action recognition
Moez Baccouche, Franck Mamalet, Christian Wolf, Christophe Garcia, and Atilla Baskurt · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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The language of actions: Recovering the syntax and semantics of goal-directed human activities
Hilde Kuehne, Ali Arslan, and Thomas Serre · 2014
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Two-stream convolutional networks for action recognition in videos
Karen Simonyan and Andrew Zisserman · 2014
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Learning spatiotemporal features with 3d convolutional networks
Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri · 2015
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Differential recurrent neural networks for action recognition
Vivek Veeriah, Naifan Zhuang, and Guo-Jun Qi · 2015
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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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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
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Spatiotemporal multiplier networks for video action recognition
Christoph Feichtenhofer, Axel Pinz, and Richard P Wildes · 2017
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Temporal residual networks for dynamic scene recognition
Christoph Feichtenhofer, Axel Pinz, and Richard P Wildes · 2017
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Accurate, large minibatch sgd: Training imagenet in 1 hour
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 2017
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Batch renormalization: Towards reducing minibatch dependence in batch-normalized models
Sergey Ioffe · 2017
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The kinetics human action video dataset
Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, et al · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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View adaptive recurrent neural networks for high performance human action recognition from skeleton data
Pengfei Zhang, Cuiling Lan, Junliang Xing, Wenjun Zeng, Jianru Xue, and Nanning Zheng · 2017
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A short note about kinetics-600
Joao Carreira, Eric Noland, Andras Banki-Horvath, Chloe Hillier, and Andrew Zisserman · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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A closer look at spatiotemporal convolutions for action recognition
Du Tran, Heng Wang, Lorenzo Torresani, Jamie Ray, Yann LeCun, and Manohar Paluri · 2018
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Transformer-xl: Attentive language models beyond a fixed-length context
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V Le, and Ruslan Salakhutdinov · 2019
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Timeception for complex action recognition
Noureldien Hussein, Efstratios Gavves, and Arnold WM Smeulders · 2019
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Videograph: Recognizing minutes-long human activities in videos
Noureldien Hussein, Efstratios Gavves, and Arnold WM Smeulders · 2019
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Scsampler: Sampling salient clips from video for efficient action recognition
Bruno Korbar, Du Tran, and Lorenzo Torresani · 2019
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Howto100m: Learning a text-video embedding by watching hundred million narrated video clips
Antoine Miech, Dimitri Zhukov, Jean-Baptiste Alayrac, Makarand Tapaswi, Ivan Laptev, and Josef Sivic · 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, et al · 2019
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Videobert: A joint model for video and language representation learning
Chen Sun, Austin Myers, Carl Vondrick, Kevin Murphy, and Cordelia Schmid · 2019
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Coin: A large-scale dataset for comprehensive instructional video analysis
Yansong Tang, Dajun Ding, Yongming Rao, Yu Zheng, Danyang Zhang, Lili Zhao, Jiwen Lu, and Jie Zhou · 2019
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Coin: A large-scale dataset for comprehensive instructional video analysis
Yansong Tang, Dajun Ding, Yongming Rao, Yu Zheng, Danyang Zhang, Lili Zhao, Jiwen Lu, and Jie Zhou · 2019
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Video classification with channel-separated convolutional networks
Du Tran, Heng Wang, Lorenzo Torresani, and Matt Feiszli · 2019
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Combining recurrent, convolutional, and continuous-time models with linear state space layers
Albert Gu, Isys Johnson, Karan Goel, Khaled Saab, Tri Dao, Atri Rudra, and Christopher Ré · 2021
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Vx2text: End-to-end learning of video-based text generation from multimodal inputs
Xudong Lin, Gedas Bertasius, Jue Wang, Shih-Fu Chang, Devi Parikh, and Lorenzo Torresani · 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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Ze Liu, Jia Ning, Yue Cao, Yixuan Wei, Zheng Zhang, Stephen Lin, and Han Hu · 2021
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Adaframe: Adaptive frame selection for fast video recognition
Zuxuan Wu, Caiming Xiong, Chih-Yao Ma, Richard Socher, and Larry S Davis · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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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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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2020
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Rethinking attention with performers
Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Davis, Afroz Mohiuddin, Lukasz Kaiser, et al · 2020
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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 · 2020
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Daniel Neimark, Omri Bar, Maya Zohar, and Dotan Asselmann · 2021
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Scalable vision transformers with hierarchical pooling
Zizheng Pan, Bohan Zhuang, Jing Liu, Haoyu He, and Jianfei Cai · 2021
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Keeping your eye on the ball: Trajectory attention in video transformers
Mandela Patrick, Dylan Campbell, Yuki Asano, Ishan Misra, Florian Metze, Christoph Feichtenhofer, Andrea Vedaldi, and João F Henriques · 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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Broaden your views for self-supervised video learning
Adrià Recasens, Pauline Luc, Jean-Baptiste Alayrac, Luyu Wang, Florian Strub, Corentin Tallec, Mateusz Malinowski, Viorica Patraucean, Florent Altché, Michal Valko, et al · 2021
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Efficient video transformers with spatial-temporal token selection
Junke Wang, Xitong Yang, Hengduo Li, Zuxuan Wu, and Yu-Gang Jiang · 2021
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Towards Long-Form Video Understanding
Chao-Yuan Wu and Philipp Krähenbühl · 2021
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Adavit: Adaptive tokens for efficient vision transformer
Hongxu Yin, Arash Vahdat, Jose Alvarez, Arun Mallya, Jan Kautz, and Pavlo Molchanov · 2021
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Graph-based high-order relation modeling for long-term action recognition
Jiaming Zhou, Kun-Yu Lin, Haoxin Li, and Wei-Shi Zheng · 2021
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Masked autoencoders as spatiotemporal learners
Christoph Feichtenhofer, Haoqi Fan, Yanghao Li, and Kaiming He · 2022
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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Long movie clip classification with state-space video models
Md Mohaiminul Islam and Gedas Bertasius · 2022
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Not all patches are what you need: Expediting vision transformers via token reorganizations
Youwei Liang, Chongjian Ge, Zhan Tong, Yibing Song, Jue Wang, and Pengtao Xie · 2022
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Learning to recognize procedural activities with distant supervision
Xudong Lin, Fabio Petroni, Gedas Bertasius, Marcus Rohrbach, Shih-Fu Chang, and Lorenzo Torresani · 2022
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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 · 2022
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S4nd: Modeling images and videos as multidimensional signals using state spaces
Eric Nguyen, Karan Goel, Albert Gu, Gordon W Downs, Preey Shah, Tri Dao, Stephen A Baccus, and Christopher Ré · 2022
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Long-form video-language pre-training with multimodal temporal contrastive learning
Yuchong Sun, Bei Liu, Hongwei Xue, Ruihua Sone, Huan Yang, and Jianlong Fu · 2022
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Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training
Zhan Tong, Yibing Song, Jue Wang, and Limin Wang · 2022
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Long-short temporal contrastive learning of video transformers
Jue Wang, Gedas Bertasius, Du Tran, and Lorenzo Torresani · 2022
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Deformable video transformer
Jue Wang and Lorenzo Torresani · 2022
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Memvit: Memory-augmented multiscale vision transformer for efficient long-term video recognition
Chao-Yuan Wu, Yanghao Li, Karttikeya Mangalam, Haoqi Fan, Bo Xiong, Jitendra Malik, and Christoph Feichtenhofer · 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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