CLASP: Cross-modal Salient Anchor-based Semantic Propagation for Weakly-supervised Dense Audio-Visual Event Localization

• URL: http://arxiv.org/abs/2508.04566v1
• Date: Wed, 06 Aug 2025 15:49:53 GMT
• Title: CLASP: Cross-modal Salient Anchor-based Semantic Propagation for Weakly-supervised Dense Audio-Visual Event Localization
• Authors: Jinxing Zhou, Ziheng Zhou, Yanghao Zhou, Yuxin Mao, Zhangling Duan, Dan Guo,
• Abstract summary: This paper explores DAVEL under a new and more challenging weakly-supervised setting (W-DAVEL task)<n>We exploit textitcross-modal salient anchors, which are defined as reliable timestamps that are well predicted under weak supervision.<n>We establish benchmarks for W-DAVEL on both the UnAV-100 and ActivityNet1.3 datasets.
• Score: 15.861700882671418
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Dense Audio-Visual Event Localization (DAVEL) task aims to temporally localize events in untrimmed videos that occur simultaneously in both the audio and visual modalities. This paper explores DAVEL under a new and more challenging weakly-supervised setting (W-DAVEL task), where only video-level event labels are provided and the temporal boundaries of each event are unknown. We address W-DAVEL by exploiting \textit{cross-modal salient anchors}, which are defined as reliable timestamps that are well predicted under weak supervision and exhibit highly consistent event semantics across audio and visual modalities. Specifically, we propose a \textit{Mutual Event Agreement Evaluation} module, which generates an agreement score by measuring the discrepancy between the predicted audio and visual event classes. Then, the agreement score is utilized in a \textit{Cross-modal Salient Anchor Identification} module, which identifies the audio and visual anchor features through global-video and local temporal window identification mechanisms. The anchor features after multimodal integration are fed into an \textit{Anchor-based Temporal Propagation} module to enhance event semantic encoding in the original temporal audio and visual features, facilitating better temporal localization under weak supervision. We establish benchmarks for W-DAVEL on both the UnAV-100 and ActivityNet1.3 datasets. Extensive experiments demonstrate that our method achieves state-of-the-art performance.

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