TEn-CATG:Text-Enriched Audio-Visual Video Parsing with Multi-Scale Category-Aware Temporal Graph

• URL: http://arxiv.org/abs/2509.04086v2
• Date: Mon, 27 Oct 2025 14:28:49 GMT
• Title: TEn-CATG:Text-Enriched Audio-Visual Video Parsing with Multi-Scale Category-Aware Temporal Graph
• Authors: Yaru Chen, Faegheh Sardari, Peiliang Zhang, Ruohao Guo, Yang Xiang, Zhenbo Li, Wenwu Wang,
• Abstract summary: TEn-CATG is a text-enriched AVVP framework that combines semantic calibration with category-aware temporal reasoning.<n>We show that TEn-CATG achieves robustness and superior ability to capture complex temporal and semantic dependencies in weakly supervised AVVP tasks.
• Score: 28.536724593429398
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Audio-visual video parsing (AVVP) aims to detect event categories and their temporal boundaries in videos, typically under weak supervision. Existing methods mainly focus on (i) improving temporal modeling using attention-based architectures or (ii) generating richer pseudo-labels to address the absence of frame-level annotations. However, attention-based models often overfit noisy pseudo-labels, leading to cumulative training errors, while pseudo-label generation approaches distribute attention uniformly across frames, weakening temporal localization accuracy. To address these challenges, we propose TEn-CATG, a text-enriched AVVP framework that combines semantic calibration with category-aware temporal reasoning. More specifically, we design a bi-directional text fusion (BiT) module by leveraging audio-visual features as semantic anchors to refine text embeddings, which departs from conventional text-to-feature alignment, thereby mitigating noise and enhancing cross-modal consistency. Furthermore, we introduce the category-aware temporal graph (CATG) module to model temporal relationships by selecting multi-scale temporal neighbors and learning category-specific temporal decay factors, enabling effective event-dependent temporal reasoning. Extensive experiments demonstrate that TEn-CATG achieves state-of-the-art results across multiple evaluation metrics on benchmark datasets LLP and UnAV-100, highlighting its robustness and superior ability to capture complex temporal and semantic dependencies in weakly supervised AVVP tasks.

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