Fetching the paper…
Reading the bibliography…
We present a multiview pseudo-labeling approach to video learning, a novel framework that uses complementary views in the form of appearance and motion information for semi-supervised learning in video.
Determining optical flow
Berthold KP Horn and Brian G Schunck · 1981
Earlier work this paper cites.
Combining labeled and unlabeled data with co-training
Avrim Blum and Tom Mitchell · 1998
Earlier work this paper cites.
Lucas/kanade meets horn/schunck: Combining local and global optic flow methods
Andrés Bruhn, Joachim Weickert, and Christoph Schnörr · 2005
Earlier work this paper cites.
Histograms of oriented gradients for human detection
Navneet Dalal and Bill Triggs · 2005
Earlier work this paper cites.
Behavior recognition via sparse spatio-temporal features
P. Dollár, V. Rabaud, G. Cottrell, and S. Belongie · 2005
Earlier work this paper cites.
Semi-supervised learning by entropy minimization
Yves Grandvalet and Yoshua Bengio · 2005
Earlier work this paper cites.
Human detection using oriented histograms of flow and appearance
Navneet Dalal, Bill Triggs, and Cordelia Schmid · 2006
Earlier work this paper cites.
A spatio-temporal descriptor based on 3d-gradients
Alexander Kläser, Marcin Marszałek, and Cordelia Schmid · 2008
Earlier work this paper cites.
Beyond pixels: exploring new representations and applications for motion analysis
Ce Liu et al · 2009
Earlier work this paper cites.
HMDB: A large video database for human motion recognition
H. Kuehne, H. Jhuang, E. Garrote, T. Poggio, and T. 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
Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah · 2012
Earlier work this paper cites.
Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee · 2013
Earlier work this paper cites.
Action recognition with improved trajectories
Heng Wang and Cordelia Schmid · 2013
Earlier work this paper cites.
Two-stream convolutional networks for action recognition in videos
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 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.
Learning spatiotemporal features with 3d convolutional networks
Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri · 2015
Earlier work this paper cites.
Convolutional two-stream network fusion for video action recognition
Christoph Feichtenhofer, Axel Pinz, and Andrew Zisserman · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
Earlier work this paper cites.
Regularization with stochastic transformations and perturbations for deep semi-supervised learning
Mehdi Sajjadi, Mehran Javanmardi, and Tolga Tasdizen · 2016
Earlier work this paper cites.
Much ado about time: Exhaustive annotation of temporal data
Gunnar A Sigurdsson, Olga Russakovsky, Ali Farhadi, Ivan Laptev, and Abhinav Gupta · 2016
Cited alongside, same era.
Temporal segment networks: Towards good practices for deep action recognition
Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaoou Tang, and Luc Van Gool · 2016
Cited alongside, same era.
Quo vadis, action recognition? a new model and the kinetics dataset
Joao Carreira and Andrew Zisserman · 2017
Cited alongside, same era.
Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W Taylor · 2017
Cited alongside, same era.
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
Self-supervised spatiotemporal learning via video clip order prediction
Dejing Xu, Jun Xiao, Zhou Zhao, Jian Shao, Di Xie, and Yueting Zhuang · 2019
Later among the works it cites.
Billion-scale semi-supervised learning for image classification
I Zeki Yalniz, Hervé Jégou, Kan Chen, Manohar Paluri, and Dhruv Mahajan · 2019
Later among the works it cites.
Speednet: Learning the speediness in videos
Sagie Benaim, Ariel Ephrat, Oran Lang, Inbar Mosseri, William T Freeman, Michael Rubinstein, Michal Irani, and Tali Dekel · 2020
Later among the works it cites.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Later among the works it cites.
Big self-supervised models are strong semi-supervised learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey Hinton · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
The” something something” video database for learning and evaluating visual common sense
Raghav Goyal, Samira Ebrahimi Kahou, Vincent Michalski, Joanna Materzynska, Susanne Westphal, Heuna Kim, Valentin Haenel, Ingo Fruend, Peter Yianilos, Moritz Mueller-Freitag, et al · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2017
Cited alongside, same era.
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola · 2017
Cited alongside, same era.
Semi-supervised convolutional neural networks for human activity recognition
Ming Zeng, Tong Yu, Xiao Wang, Le T Nguyen, Ole J Mengshoel, and Ian Lane · 2017
Cited alongside, same era.
A short note about kinetics-600
Joao Carreira, Eric Noland, Andras Banki-Horvath, Chloe Hillier, and Andrew Zisserman · 2018
Cited alongside, same era.
Cooperative learning of audio and video models from self-supervised synchronization
Bruno Korbar, Du Tran, and Lorenzo Torresani · 2018
Cited alongside, same era.
Randaugment: Practical automated data augmentation with a reduced search space
Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le · 2020
Later among the works it cites.
PySlowFast
Haoqi Fan, Yanghao Li, Bo Xiong, Wan-Yen Lo, and Christoph Feichtenhofer · 2020
Later among the works it cites.
Self-supervised co-training for video representation learning
Tengda Han, Weidi Xie, and Andrew Zisserman · 2020
Later among the works it cites.
Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
Later among the works it cites.
Data-efficient image recognition with contrastive predictive coding
Olivier J. Hénaff, Aravind Srinivas, Jeffrey De Fauw, Ali Razavi, Carl Doersch, S. M. Ali Eslami, and Aaron van den Oord · 2020
Later among the works it cites.
Videossl: Semi-supervised learning for video classification
Longlong Jing, Toufiq Parag, Zhe Wu, Yingli Tian, and Hongcheng Wang · 2020
Later among the works it cites.
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
Later among the works it cites.
Audio-visual instance discrimination with cross-modal agreement
Pedro Morgado, Vasconcelos Nuno, and Misra Ishan · 2020
Later among the works it cites.
Multi-modal Domain Adaptation for Fine-grained Action Recognition
Jonathan Munro and Dima Damen · 2020
Later among the works it cites.
Multi-modal self-supervision from generalized data transformations
Mandela Patrick, Yuki M Asano, Ruth Fong, João F Henriques, Geoffrey Zweig, and Andrea Vedaldi · 2020
Later among the works it cites.
Spatiotemporal contrastive video representation learning
Rui Qian, Tianjian Meng, Boqing Gong, Ming-Hsuan Yang, Huisheng Wang, Serge Belongie, and Yin Cui · 2020
Later among the works it cites.
A short note on the kinetics-700-2020 human action dataset
Lucas Smaira, João Carreira, Eric Noland, Ellen Clancy, Amy Wu, and Andrew Zisserman · 2020
Later among the works it cites.
Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D Cubuk, Alex Kurakin, Han Zhang, and Colin Raffel · 2020
Later among the works it cites.
Audiovisual slowfast networks for video recognition
Fanyi Xiao, Yong Jae Lee, Kristen Grauman, Jitendra Malik, and Christoph Feichtenhofer · 2020
Later among the works it cites.
Self-training with noisy student improves imagenet classification
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V. Le · 2020
Later among the works it cites.
Video representation learning with visual tempo consistency
Ceyuan Yang, Yinghao Xu, Bo Dai, and Bolei Zhou · 2020
Later among the works it cites.
Actbert: Learning global-local video-text representations
Linchao Zhu and Yi Yang · 2020
Later among the works it cites.