@Article{cmc.2020.09867,
AUTHOR = {Shuren Zhou, Le Chen, Vijayan Sugumaran},
TITLE = {Hidden Two-Stream Collaborative Learning Network for Action  Recognition},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {63},
YEAR = {2020},
NUMBER = {3},
PAGES = {1545--1561},
URL = {http://www.techscience.com/cmc/v63n3/38892},
ISSN = {1546-2226},
ABSTRACT = {The two-stream convolutional neural network exhibits excellent performance 
in the video action recognition. The crux of the matter is to use the frames already 
clipped by the videos and the optical flow images pre-extracted by the frames, to train a 
model each, and to finally integrate the outputs of the two models. Nevertheless, the 
reliance on the pre-extraction of the optical flow impedes the efficiency of action 
recognition, and the temporal and the spatial streams are just simply fused at the ends, 
with one stream failing and the other stream succeeding. We propose a novel hidden twostream collaborative (HTSC) learning network that masks the steps of extracting the 
optical flow in the network and greatly speeds up the action recognition. Based on the 
two-stream method, the two-stream collaborative learning model captures the interaction 
of the temporal and spatial features to greatly enhance the accuracy of recognition. Our 
proposed method is highly capable of achieving the balance of efficiency and precision 
on large-scale video action recognition datasets.},
DOI = {10.32604/cmc.2020.09867}
}