Gate-Shift-Fuse for Video Action Recognition
Author:
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
Link
http://xplorestaging.ieee.org/ielx7/34/10210213/10105518.pdf?arnumber=10105518
Reference82 articles.
1. SlowFast Networks for Video Recognition
2. Optical Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition
3. X3D: Expanding Architectures for Efficient Video Recognition
4. Human Action Recognition Using Factorized Spatio-Temporal Convolutional Networks
5. Spatiotemporal Multiplier Networks for Video Action Recognition
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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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