Fetching the paper…
Reading the bibliography…
This paper presents A3D, an adaptive 3D network that can infer at a wide range of computational constraints with one-time training.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
Earlier work this paper cites.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 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.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Hollywood in homes: Crowdsourcing data collection for activity understanding
Gunnar A Sigurdsson, Gül Varol, Xiaolong Wang, Ali Farhadi, Ivan Laptev, and Abhinav Gupta · 2016
Earlier work this paper cites.
Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 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.
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
Earlier work this paper cites.
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
Earlier work this paper cites.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Learning spatio-temporal representation with pseudo-3d residual networks
Zhaofan Qiu, Ting Yao, and Tao Mei · 2017
Earlier work this paper cites.
Searching for activation functions
Prajit Ramachandran, Barret Zoph, and Quoc V Le · 2017
Earlier work this paper cites.
Asynchronous temporal fields for action recognition
Gunnar A Sigurdsson, Santosh Divvala, Ali Farhadi, and Abhinav Gupta · 2017
Earlier work this paper cites.
Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
Earlier work this paper cites.
Multi-fiber networks for video recognition
Yunpeng Chen, Yannis Kalantidis, Jianshu Li, Shuicheng Yan, and Jiashi Feng · 2018
Earlier work this paper cites.
Ava: A video dataset of spatio-temporally localized atomic visual actions
Chunhui Gu, Chen Sun, David A Ross, Carl Vondrick, Caroline Pantofaru, Yeqing Li, Sudheendra Vijayanarasimhan, George Toderici, Susanna Ricco, Rahul Sukthankar, et al · 2018
Cited alongside, same era.
Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet?
Kensho Hara, Hirokatsu Kataoka, and Yutaka Satoh · 2018
Cited alongside, same era.
Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
Cited alongside, same era.
Human centric spatio-temporal action localization
Jianwen Jiang, Yu Cao, Lin Song, Shiwei Zhang4 Yunkai Li, Ziyao Xu, Qian Wu, Chuang Gan, Chi Zhang, and Gang Yu · 2018
Cited alongside, same era.
Human centric spatio-temporal action localization
Jianwen Jiang, Yu Cao, Lin Song, Shiwei Zhang4 Yunkai Li, Ziyao Xu, Qian Wu, Chuang Gan, Chi Zhang, and Gang Yu · 2018
Cited alongside, same era.
Shufflenet v2: Practical guidelines for efficient cnn architecture design
Tsm: Temporal shift module for efficient video understanding
Ji Lin, Chuang Gan, and Song Han · 2019
Later among the works it cites.
Grouped spatial-temporal aggregation for efficient action recognition
Chenxu Luo and Alan L Yuille · 2019
Later among the works it cites.
Video action recognition via neural architecture searching
Wei Peng, Xiaopeng Hong, and Guoying Zhao · 2019
Later among the works it cites.
Evolving space-time neural architectures for videos
AJ Piergiovanni, Anelia Angelova, Alexander Toshev, and Michael S Ryoo · 2019
Later among the works it cites.
Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
Later among the works it cites.
Video classification with channel-separated convolutional networks
Du Tran, Heng Wang, Lorenzo Torresani, and Matt Feiszli · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, and Jian Sun · 2018
Cited alongside, same era.
Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
Cited alongside, same era.
Actor-centric relation network
Chen Sun, Abhinav Shrivastava, Carl Vondrick, Kevin Murphy, Rahul Sukthankar, and Cordelia Schmid · 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.
Non-local neural networks
Xiaolong Wang, Ross Girshick, Abhinav Gupta, and Kaiming He · 2018
Cited alongside, same era.
Compressed video action recognition
Chao-Yuan Wu, Manzil Zaheer, Hexiang Hu, R Manmatha, Alexander J Smola, and Philipp Krähenbühl · 2018
Cited alongside, same era.
Rethinking spatiotemporal feature learning: Speed-accuracy trade-offs in video classification
Saining Xie, Chen Sun, Jonathan Huang, Zhuowen Tu, and Kevin Murphy · 2018
Cited alongside, same era.
Later among the works it cites.
Adaframe: Adaptive frame selection for fast video recognition
Zuxuan Wu, Caiming Xiong, Chih-Yao Ma, Richard Socher, and Larry S Davis · 2019
Later among the works it cites.
Universally slimmable networks and improved training techniques
Jiahui Yu and Thomas S Huang · 2019
Later among the works it cites.
X3d: Expanding architectures for efficient video recognition
Christoph Feichtenhofer · 2020
Closest in time.
Multi-dimensional pruning: A unified framework for model compression
Jinyang Guo, Wanli Ouyang, and Dong Xu · 2020
Closest in time.
Ar-net: Adaptive frame resolution for efficient action recognition
Yue Meng, Chung-Ching Lin, Rameswar Panda, Prasanna Sattigeri, Leonid Karlinsky, Aude Oliva, Kate Saenko, and Rogerio Feris · 2020
Closest in time.
Attentionnas: Spatiotemporal attention cell search for video classification
Xiaofang Wang, Xuehan Xiong, Maxim Neumann, AJ Piergiovanni, Michael S Ryoo, Anelia Angelova, Kris M Kitani, and Wei Hua · 2020
Closest in time.
G-tad: Sub-graph localization for temporal action detection
Mengmeng Xu, Chen Zhao, David S Rojas, Ali Thabet, and Bernard Ghanem · 2020
Closest in time.
Mutualnet: Adaptive convnet via mutual learning from network width and resolution
Taojiannan Yang, Sijie Zhu, Chen Chen, Shen Yan, Mi Zhang, and Andrew Willis · 2020
Closest in time.
Bignas: Scaling up neural architecture search with big single-stage models
Jiahui Yu, Pengchong Jin, Hanxiao Liu, Gabriel Bender, Pieter-Jan Kindermans, Mingxing Tan, Thomas Huang, Xiaodan Song, Ruoming Pang, and Quoc Le · 2020
Closest in time.
Bottom-up temporal action localization with mutual regularization
Peisen Zhao, Lingxi Xie, Chen Ju, Ya Zhang, Yanfeng Wang, and Qi Tian · 2020
Closest in time.