Proposal Score Realignment Guided by Semantic Completeness for Weakly Supervised Temporal Action Localization

Due to the absence of temporal annotations, recent weaklysupervised temporal action localization (WTAL) methods often adoptpseudo-label learning to boost localization. A segment-level WTAL base-line first generates proposals and constructs pseudo labels, which thensupervise a fully supervised localization head. However, the constructionof pseudo labels depends on the proposals’ original confidence scores.Accumulated noise in proposal generation induces a mismatch betweenthese confidence scores and proposal quality, thereby limiting the achiev-able quality of the pseudo labels. To address this, we propose SCLR, aproposal-level score realignment framework driven by Semantic Com-pleteness Learning (SCL). SCL proceeds in three steps. First, for eachaction class, we construct a core semantic center that focuses on localhigh-response cues and a global teacher center that aggregates class-levelcontextual commonalities. Second, guided by the two centers, we self-supervise the decomposition of the core semantic center into generic andspecific components, then reconstruct the class center by a weighted sum.Third, we derive soft labels from proposal–to–center similarity to super-vise a per-proposal semantic-completeness score, which is then used forscore realignment. Extensive experiments on two public datasets demon-strate that SCLR, when integrated as a plug-and-play framework, consis-tently reduces score misalignment across diverse baselines and improveslocalization accuracy. Further, leveraging re-aligned proposals as pseudolabels to train the localization head yields state-of-the-art performance.