Learning to Localize Actions from Moments

Action Herald Networks (AherNet) integrate such design into an one-stage action localization framework . The context of each moment is learnt through the adversarial mechanism to differentiate the generated features from those of background in untrimmed videos . Extensive experiments are conducted on the learning across the learning both across the splits of ActivityNet v1.3 and from THUMOS14 to ActivityNet . Our AherNet demonstrates the superiority even comparing to most fully-supervised action localization methods, according to the authors . Source code and data are available at \url{https://://://www.cnn.com/FuchenUSTC/Ahernet. The paper also includes data from Kinetics-600 and activityNet v 1.3.2.1.4.5.2 . The authors also release a version of this article with a copy of the original version of the above article as it has been published by the authors of this version of The New Atlas Atlas of the New Atlas of Atlas of The Atlas Of The Atlas of Action Networks, which is available at http://http://://cnnnn.org//cnnn.uk/fuchenUSCCnn.org. The Atlas/

Links: PDF - Abstract

Code :

https://github.com/FuchenUSTC/AherNet

Keywords : atlas - action - activitynet - version - ahernet -

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