Background-Aware Robust Context Learning for Weakly-Supervised Temporal Action Localization

Background-Aware Robust Context Learning for Weakly-Supervised Temporal Action Localization

Weakly supervised temporal action localization (WTAL) aims to localize temporal intervals of actions in an untrimmed video using only video-level action labels. Although the learning of the background is an important issue in WTAL, most previous studies have not utilized an effective background. In...

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Bibliographic Details
Main Authors: Jinah Kim, Jungchan Cho
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Temporal action localization
entropy maximization
context learning
feature adaptation
Online Access:https://ieeexplore.ieee.org/document/9797701/

Internet

https://ieeexplore.ieee.org/document/9797701/

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