Abstract:Weakly supervised temporal action localization aims to detect and localize actions in untrimmed videos with only video-level labels during training. However, without framelevel annotations, it is challenging to achieve localization completeness and relieve background interference. In this paper, we present an Action Unit Memory Network (AUMN) for weakly supervised temporal action localization, which can mitigate the above two challenges by learning an action unit memory bank. In the proposed AUMN, two attentio… Show more
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