  Weakly-supervised temporal action localization (WTAL) intends to detect
action instances with only weak supervision, e.g., video-level labels. The
current~\textit{de facto} pipeline locates action instances by thresholding and
grouping continuous high-score regions on temporal class activation sequences.
In this route, the capacity of the model to recognize the relationships between
adjacent snippets is of vital importance which determines the quality of the
action boundaries. However, it is error-prone since the variations between
adjacent snippets are typically subtle, and unfortunately this is overlooked in
the literature. To tackle the issue, we propose a novel WTAL approach named
Convex Combination Consistency between Neighbors (C$^3$BN). C$^3$BN consists of
two key ingredients: a micro data augmentation strategy that increases the
diversity in-between adjacent snippets by convex combination of adjacent
snippets, and a macro-micro consistency regularization that enforces the model
to be invariant to the transformations~\textit{w.r.t.} video semantics, snippet
predictions, and snippet representations. Consequently, fine-grained patterns
in-between adjacent snippets are enforced to be explored, thereby resulting in
a more robust action boundary localization. Experimental results demonstrate
the effectiveness of C$^3$BN on top of various baselines for WTAL with
video-level and point-level supervisions. Code is at
https://github.com/Qinying-Liu/C3BN.
