Semantic Noise Reduction via Teacher-Guided Dual-Path Audio-Visual Representation Learning
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866911578453966848 |
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| author | Wang, Linge Chen, Yingying Zhu, Bingke Zhou, Lu Wang, Jinqiao |
| author_facet | Wang, Linge Chen, Yingying Zhu, Bingke Zhou, Lu Wang, Jinqiao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_08147 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Semantic Noise Reduction via Teacher-Guided Dual-Path Audio-Visual Representation Learning Wang, Linge Chen, Yingying Zhu, Bingke Zhou, Lu Wang, Jinqiao Sound Computer Vision and Pattern Recognition 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. |
| title | Semantic Noise Reduction via Teacher-Guided Dual-Path Audio-Visual Representation Learning |
| topic | Sound Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.08147 |