  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 involves partitioning the label space
of the predicted class distribution into distinct subspaces: target class,
positive classes, negative classes, and ambiguous classes, aiming to mine both
positive and negative semantics that are absent in the target class, while
excluding ambiguous classes. To this end, we first devise innovative strategies
to adaptively select high-quality positive and negative classes from the label
space, by modeling both the confidence and rank of a class in relation to those
of the target class. Then, we introduce novel positive and negative losses
designed to guide the learning process, pushing predictions closer to positive
classes and away from negative classes. Finally, the positive and negative
processes are integrated into a hybrid positive-negative learning framework,
facilitating the utilization of non-target classes in both labeled and
unlabeled videos. Experimental results on THUMOS14 and ActivityNet v1.3
demonstrate the superiority of the proposed method over prior state-of-the-art
approaches.
