Video‐based action recognition using spurious‐3D residual attention networks-Reference-Cited by-同舟云学术

Video‐based action recognition using spurious‐3D residual attention networks

Published:2022-05-26 Issue:11 Volume:16 Page:3097-3111
ISSN:1751-9659
Container-title:IET Image Processing
language:en
Short-container-title:IET Image Processing

Author:

Chen Bo¹²^ORCID,Tang Hongying¹,Zhang Zebin¹,Tong Guanjun¹,Li Baoqing¹

Affiliation:

1. Science and Technology on Microsystem Laboratory, Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China

2. School of Electronic, Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/ipr2.12541

Reference49 articles.

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2. Simonyan K. Zisserman A.:Two‐stream convolutional networks for action recognition in videos. In:28th Conference on Neural Information Processing Systems (NIPS) Montreal Canada(2014)

3. Du T. Bourdev L. Fergus R. Torresani L. Paluri M.:Learning spatiotemporal features with 3D convolutional networks. In:IEEE International Conference on Computer Vision Santiago Chile(2015)

4. Carreira J. Zisserman A.:Quo vadis action recognition? A new model and the kinetics dataset. In:30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Honolulu HI(2017)

5. Wang L. Xiong Y. Wang Z. Qiao Y. Lin D. Tang X. et al.:Temporal segment networks: Towards good practices for deep action recognition. In:14th European Conference on Computer Vision (ECCV) Amsterdam Netherlands(2016)

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