  Weakly-supervised temporal action localization aims to recognize and localize
action segments in untrimmed videos given only video-level action labels for
training. Without the boundary information of action segments, existing methods
mostly rely on multiple instance learning (MIL), where the predictions of
unlabeled instances (i.e., video snippets) are supervised by classifying
labeled bags (i.e., untrimmed videos). However, this formulation typically
treats snippets in a video as independent instances, ignoring the underlying
temporal structures within and across action segments. To address this problem,
we propose \system, a novel WTAL framework that enables explicit, action-aware
segment modeling beyond standard MIL-based methods. Our framework entails three
segment-centric components: (i) dynamic segment sampling for compensating the
contribution of short actions; (ii) intra- and inter-segment attention for
modeling action dynamics and capturing temporal dependencies; (iii) pseudo
instance-level supervision for improving action boundary prediction.
Furthermore, a multi-step refinement strategy is proposed to progressively
improve action proposals along the model training process. Extensive
experiments on THUMOS-14 and ActivityNet-v1.3 demonstrate the effectiveness of
our approach, establishing new state of the art on both datasets. The code and
models are publicly available at~\url{https://github.com/boheumd/ASM-Loc}.
