CAE-AV: Improving Audio-Visual Learning via Cross-modal Interactive Enrichment

• URL: http://arxiv.org/abs/2602.08309v1
• Date: Mon, 09 Feb 2026 06:30:25 GMT
• Title: CAE-AV: Improving Audio-Visual Learning via Cross-modal Interactive Enrichment
• Authors: Yunzuo Hu, Wen Li, Jing Zhang,
• Abstract summary: We propose a novel Caption-aligned and Agreement-guided Enhancement framework (CAE-AV) for audio-visual learning.<n>Two complementary modules: Cross-modal Agreement-guided Spatio-Temporal Enrichment (CASTE) and Caption-Aligned Saliency-guided Enrichment (CASE)<n>CASTE balances spatial and temporal relations by evaluating frame-level audio-visual agreement, ensuring that key information is captured from both preceding and subsequent frames under misalignment.<n>CASE injects cross-modal semantic guidance into selectedtemporal positions, leveraging high-level semantic cues to further alleviate misalignment.
• Score: 12.793962173450494
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Audio-visual learning suffers from modality misalignment caused by off-screen sources and background clutter, and current methods usually amplify irrelevant regions or moments, leading to unstable training and degraded representation quality. To address this challenge, we proposed a novel Caption-aligned and Agreement-guided Enhancement framework (CAE-AV) for audio-visual learning, which used two complementary modules: Cross-modal Agreement-guided Spatio-Temporal Enrichment (CASTE) and Caption-Aligned Saliency-guided Enrichment (CASE) to relieve audio-visual misalignment. CASTE dynamically balances spatial and temporal relations by evaluating frame-level audio-visual agreement, ensuring that key information is captured from both preceding and subsequent frames under misalignment. CASE injects cross-modal semantic guidance into selected spatio-temporal positions, leveraging high-level semantic cues to further alleviate misalignment. In addition, we design lightweight objectives, caption-to-modality InfoNCE, visual-audio consistency, and entropy regularization to guide token selection and strengthen cross-modal semantic alignment. With frozen backbones, CAE-AV achieves state-of-the-art performance on AVE, AVVP, AVS, and AVQA benchmarks, and qualitative analyses further validate its robustness against audio-visual misalignment.

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