Gate-Shift-Fuse for Video Action Recognition

Author:

Sudhakaran Swathikiran1ORCID,Escalera Sergio2ORCID,Lanz Oswald3ORCID

Affiliation:

1. Samsung AI Center, Cambridge, U.K.

2. Computer Vision Center, Universitat de Barcelona, Barcelona, Spain

3. Free University of Bozen-Bolzano, Bolzano, Italy

Funder

Spanish projects

Libera Università di Bolzano

Amazon Catalyst

ICREA Academia Programme

NVIDIA AI Technology Centre

EMEA

CINECA Award

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. An efficient motion visual learning method for video action recognition;Expert Systems with Applications;2024-12

2. Efficient spatio-temporal network for action recognition;Journal of Real-Time Image Processing;2024-08-23

3. Convolutional transformer network for fine-grained action recognition;Neurocomputing;2024-02

4. Temporal Shift Module-Based Vision Transformer Network for Action Recognition;IEEE Access;2024

5. Multi-scale Motion Feature Integration for Action Recognition;2023 9th International Conference on Computer and Communications (ICCC);2023-12-08

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