Boosting Semi-Supervised Temporal Action Localization by Learning from Non-Target Classes

arXiv:2403.11189v1 Announce Type: new
Abstract: The crux of semi-supervised temporal action localization (SS-TAL) lies in excavating valuable information from abundant unlabeled videos. However, current approaches predominantly focus on building models that are robust to the error-prone target class (i.e, the predicted class with the highest confidence) while ignoring informative semantics within non-target classes. This paper approaches SS-TAL from a novel perspective by advocating for learning from non-target classes, transcending the conventional focus solely on the target class. The proposed approach …

abstract arxiv boosting building class confidence crux cs.cv current error focus however information lies localization robust semantics semi-supervised temporal type videos