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In this report, we describe the technical details of our submission to the 2021 EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition.
Quo vadis, action recognition? a new model and the kinetics dataset
Joao Carreira and Andrew Zisserman · 2017
Earlier work this paper cites.
Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 2017
Earlier work this paper cites.
Residual attention network for image classification
Fei Wang, Mengqing Jiang, Chen Qian, Shuo Yang, Cheng Li, Honggang Zhang, Xiaogang Wang, and Xiaoou Tang · 2017
Earlier work this paper cites.
Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
Earlier work this paper cites.
Temporal attentive alignment for large-scale video domain adaptation
Min-Hung Chen, Zsolt Kira, Ghassan AlRegib, Jaekwon Yoo, Ruxin Chen, and Jian Zheng · 2019
Cited alongside, same era.
Epic-fusion: Audio-visual temporal binding for egocentric action recognition
Evangelos Kazakos, Arsha Nagrani, Andrew Zisserman, and Dima Damen · 2019
Cited alongside, same era.
Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Antonino Furnari, Evangelos Kazakos, Jian Ma, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, et al · 2020
Cited alongside, same era.
Multi-modal domain adaptation for fine-grained action recognition
Jonathan Munro and Dima Damen · 2020
Later among the works it cites.
Understanding human hands in contact at internet scale
Dandan Shan, Jiaqi Geng, Michelle Shu, and David Fouhey · 2020
Later among the works it cites.
Modality compensation network: Cross-modal adaptation for action recognition
Sijie Song, Jiaying Liu, Yanghao Li, and Zongming Guo · 2020
Later among the works it cites.
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