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The objective of this paper is an efficient training method for video tasks.
HMDB: A large video database for human motion recognition
H. Kuehne, H. Jhuang, E. Garrote, T. Poggio, and T. Serre · 2011
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UCF101: A dataset of 101 human actions classes from videos in the wild
Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah · 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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Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2016
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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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SSD: Single shot multibox detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg · 2016
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Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krähenbühl, Jeff Donahue, Trevor Darrell, and Alexei A. Efros · 2016
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XNOR-Net: Imagenet classification using binary convolutional neural networks
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 2016
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You only look once: Unified, real-time object detection
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna · 2016
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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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The kinetics human action video dataset
Will Kay, João Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, Mustafa Suleyman, and Andrew Zisserman · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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SGDR: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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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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CornerNet: Detecting objects as paired keypoints
Hei Law and Jia Deng · 2018
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Representation learning with contrastive predictive coding
Aäron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Compressed video action recognition
Chao-Yuan Wu, Manzil Zaheer, Hexiang Hu, R. Manmatha, Alexander J. Smola, and Philipp Krähenbühl · 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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Decoupled weight decay regularization
Frank Hutter Ilya Loshchilov · 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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Large memory layers with product keys
Guillaume Lample, Alexandre Sablayrolles, Marc’Aurelio Ranzato, Ludovic Denoyer, and Hervé Jégou · 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
MPNet: Masked and permuted pre-training for language understanding
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu · 2020
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Yi Tay, Dara Bahri, Liu Yang, Donald Metzler, and Da-Cheng Juan · 2020
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Linformer: Self-attention with linear complexity
Sinong Wang, Belinda Z. Li, Madian Khabsa, Han Fang, and Hao Ma · 2020
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A multigrid method for efficiently training video models
Chao-Yuan Wu, Ross Girshick, Kaiming He, Christoph Feichtenhofer, and Philipp Krähenbühl · 2020
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Rethinking attention with performers
Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamás Sarlós, Peter Hawkins, Jared Davis, Afroz Mohiuddin, Lukasz Kaiser, David Belanger, Lucy J. Colwell, and Adrian Weller · 2021
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V. Le · 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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Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
Cited alongside, same era.
Self-supervised learning by cross-modal audio-video clustering
Humam Alwassel, Dhruv Mahajan, Lorenzo Torresani, Bernard Ghanem, and Du Tran · 2020
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Randaugment: Practical automated data augmentation with a reduced search space
Ekin Dogus Cubuk, Barret Zoph, Jon Shlens, and Quoc Le · 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, Jakob Uszkoreit, and Neil Houlsby · 2020
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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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Perceiver: General perception with iterative attention
Andrew Jaegle, Felix Gimeno, Andrew Brock, Andrew Zisserman, Oriol Vinyals, and João Carreira · 2021
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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 V. Le, Yunhsuan Sung, Zhen Li, and Tom Duerig · 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, Gretchen Krueger, and Ilya Sutskever · 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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BEiT: Bert pre-training of image transformers
Hangbo Bao, Li Dong, Songhao Piao, and Furu Wei · 2022
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Masked autoencoders as spatiotemporal learners
Christoph Feichtenhofer, Haoqi Fan, Yanghao Li, and Kaiming He · 2022
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MaskViT: Masked visual pre-training for video prediction
Agrim Gupta, Stephen Tian, Yunzhi Zhang, Jiajun Wu, Roberto Martín-Martín, and Li Fei-Fei · 2022
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Temporal alignment networks for long-term video
Tengda Han, Weidi Xie, and Andrew Zisserman · 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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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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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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SimMIM: A simple framework for masked image modeling
Zhenda, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, and Han Hu · 2022
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