Adversarial Seeded Sequence Growing for Weakly-Supervised Temporal Action Localization-Reference-Cited by-同舟云学术

Adversarial Seeded Sequence Growing for Weakly-Supervised Temporal Action Localization

Published:2019-10-15 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 27th ACM International Conference on Multimedia
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Author:

Zhang Chengwei¹,Xu Yunlu²,Cheng Zhanzhan³,Niu Yi²,Pu Shiliang⁴,Wu Fei⁵,Zou Futai¹

Affiliation:

1. Shanghai Jiao Tong University, Shanghai, China

2. Hikvision Research Institute, Shanghai, China

3. Hikvision Research Institute & Zhejiang University, Shanghai, China

4. Hikvision Research Institute, Hangzhou, China

5. Zhejiang University, Hangzhou, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3343031.3351044

Reference32 articles.

1. Joao Carreira and Andrew Zisserman. 2017. Quo Vadis Action Recognition? A New Model and the Kinetics Dataset. In CVPR . 4724--4733. Joao Carreira and Andrew Zisserman. 2017. Quo Vadis Action Recognition? A New Model and the Kinetics Dataset. In CVPR . 4724--4733.

2. Yu Wei Chao Sudheendra Vijayanarasimhan Bryan Seybold David A Ross Jia Deng and Rahul Sukthankar. 2018. Rethinking the Faster R-CNN Architecture for Temporal Action Localization. In CVPR . 2933--2942. Yu Wei Chao Sudheendra Vijayanarasimhan Bryan Seybold David A Ross Jia Deng and Rahul Sukthankar. 2018. Rethinking the Faster R-CNN Architecture for Temporal Action Localization. In CVPR . 2933--2942.

3. Xiyang Dai Bharat Singh Guyue Zhang Larry S. Davis and Yan Qiu Chen. 2017. Temporal Context Network for Activity Localization in Videos. In ICCV . 5727--5736. Xiyang Dai Bharat Singh Guyue Zhang Larry S. Davis and Yan Qiu Chen. 2017. Temporal Context Network for Activity Localization in Videos. In ICCV . 5727--5736.

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