Fetching the paper…
Reading the bibliography…
In this report, we present the Baidu-UTS submission to the EPIC-Kitchens Action Recognition Challenge in CVPR 2019.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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.
Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross B. Girshick, and Jian Sun · 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.
A discriminative cnn video representation for event detection
Zhongwen Xu, Yi Yang, and Alex G Hauptmann · 2015
Earlier work this paper cites.
Youtube-8m: A large-scale video classification benchmark
Sami Abu-El-Haija, Nisarg Kothari, Joonseok Lee, Paul Natsev, George Toderici, Balakrishnan Varadarajan, and Sudheendra Vijayanarasimhan · 2016
Earlier work this paper cites.
Visual genome: Connecting language and vision using crowdsourced dense image annotations
Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A. Shamma, Michael S. Bernstein, and Li Fei-Fei · 2016
Earlier work this paper cites.
Temporal segment networks: Towards good practices for deep action recognition
Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaoou Tang, and Luc Val Gool · 2016
Earlier work this paper cites.
Quo vadis, action recognition? a new model and the kinetics dataset
João Carreira and Andrew Zisserman · 2017
Cited alongside, same era.
Actionvlad: Learning spatio-temporal aggregation for action classification
Rohit Girdhar, Deva Ramanan, Abhinav Gupta, Josef Sivic, and Bryan Russell · 2017
Cited alongside, same era.
Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross B. Girshick · 2017
Cited alongside, same era.
Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross B. Girshick, Kaiming He, Bharath Hariharan, and Serge J. Belongie · 2017
Cited alongside, same era.
Learnable pooling with context gating for video classification
Antoine Miech, Ivan Laptev, and Josef Sivic · 2017
Cited alongside, same era.
Aggregated residual transformations for deep neural networks
Bottom-up and top-down attention for image captioning and visual question answering
Peter Anderson, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen Gould, and Lei Zhang · 2018
Later among the works it cites.
Object level visual reasoning in videos
Fabien Baradel, Natalia Neverova, Christian Wolf, Julien Mille, and Greg Mori · 2018
Later among the works it cites.
Scaling egocentric vision: The epic-kitchens dataset
Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Sanja Fidler, Antonino Furnari, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, and Michael Wray · 2018
Later among the works it cites.
Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet?
Kensho Hara, Hirokatsu Kataoka, and Yutaka Satoh · 2018
Later among the works it cites.
Large-scale weakly-supervised pre-training for video action recognition
Deepti Ghadiyaram, Du Tran, and Dhruv Mahajan · 2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Saining Xie, Ross B. Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
Cited alongside, same era.
Bidirectional multirate reconstruction for temporal modeling in videos
Linchao Zhu, Zhongwen Xu, and Yi Yang · 2017
Cited alongside, same era.
Uncovering the temporal context for video question answering
Linchao Zhu, Zhongwen Xu, Yi Yang, and Alexander G Hauptmann · 2017
Cited alongside, same era.
Swathikiran Sudhakaran, Sergio Escalera, and Oswald Lanz · 2019
Closest in time.
Long-term feature banks for detailed video understanding
Chao-Yuan Wu, Christoph Feichtenhofer, Haoqi Fan, Kaiming He, Philipp Krahenbuhl, and Ross Girshick · 2019
Closest in time.
Faster recurrent networks for video classification
Linchao Zhu, Laura Sevilla-Lara, Du Tran, Matt Feiszli, Yi Yang, and Heng Wang · 2019
Closest in time.