Semantic Noise Reduction via Teacher-Guided Dual-Path Audio-Visual Representation Learning

• URL: http://arxiv.org/abs/2604.08147v1
• Date: Thu, 09 Apr 2026 12:08:40 GMT
• Title: Semantic Noise Reduction via Teacher-Guided Dual-Path Audio-Visual Representation Learning
• Abstract summary: TG-DP is a Teacher-Guided Dual-Path framework that decouples reconstruction and alignment into separate optimization paths.<n> TG-DP achieves state-of-the-art performance in zero-shot retrieval.
• Score: 33.481995795091045
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in audio-visual representation learning have shown the value of combining contrastive alignment with masked reconstruction. However, jointly optimizing these objectives in a single forward pass forces the contrastive branch to rely on randomly visible patches designed for reconstruction rather than cross-modal alignment, introducing semantic noise and optimization interference. We propose TG-DP, a Teacher-Guided Dual-Path framework that decouples reconstruction and alignment into separate optimization paths. By disentangling the masking regimes of the two branches, TG-DP enables the contrastive pathway to use a visibility pattern better suited to cross-modal alignment. A teacher model further provides auxiliary guidance for organizing visible tokens in this branch, helping reduce interference and stabilize cross-modal representation learning. TG-DP achieves state-of-the-art performance in zero-shot retrieval. On AudioSet, it improves R@1 from 35.2\% to 37.4\% for video-to-audio retrieval and from 27.9\% to 37.1\% for audio-to-video retrieval. The learned representations also remain semantically robust, achieving state-of-the-art linear-probe performance on AS20K and VGGSound. Taken together, our results suggest that decoupling multimodal objectives and introducing teacher-guided structure into the contrastive pathway provide an effective framework for improving large-scale audio-visual pretraining. Code is available at https://github.com/wanglg20/TG-DP.

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