Discrepancy-Aware Attention Network for Enhanced Audio-Visual Zero-Shot Learning

• URL: http://arxiv.org/abs/2412.11715v1
• Date: Mon, 16 Dec 2024 12:35:56 GMT
• Title: Discrepancy-Aware Attention Network for Enhanced Audio-Visual Zero-Shot Learning
• Authors: RunLin Yu, Yipu Gong, Wenrui Li, Aiwen Sun, Mengren Zheng,
• Abstract summary: We propose a Discrepancy-Aware Attention Network (DAAN) for Enhanced Audio-Visual ZSL. Our approach introduces a Quality-Discrepancy Attention (QDMA) unit to minimize redundant information in the high-quality modality. Experiments demonstrate DAAN state-of-the-art performance on benchmark datasets.
• Score: 1.8175282137722093
• License:
• Abstract: Audio-visual Zero-Shot Learning (ZSL) has attracted significant attention for its ability to identify unseen classes and perform well in video classification tasks. However, modal imbalance in (G)ZSL leads to over-reliance on the optimal modality, reducing discriminative capabilities for unseen classes. Some studies have attempted to address this issue by modifying parameter gradients, but two challenges still remain: (a) Quality discrepancies, where modalities offer differing quantities and qualities of information for the same concept. (b) Content discrepancies, where sample contributions within a modality vary significantly. To address these challenges, we propose a Discrepancy-Aware Attention Network (DAAN) for Enhanced Audio-Visual ZSL. Our approach introduces a Quality-Discrepancy Mitigation Attention (QDMA) unit to minimize redundant information in the high-quality modality and a Contrastive Sample-level Gradient Modulation (CSGM) block to adjust gradient magnitudes and balance content discrepancies. We quantify modality contributions by integrating optimization and convergence rate for more precise gradient modulation in CSGM. Experiments demonstrates DAAN achieves state-of-the-art performance on benchmark datasets, with ablation studies validating the effectiveness of individual modules.

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