MA-AVT: Modality Alignment for Parameter-Efficient Audio-Visual Transformers
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916279593467904 |
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| author | Mahmud, Tanvir Mo, Shentong Tian, Yapeng Marculescu, Diana |
| author_facet | Mahmud, Tanvir Mo, Shentong Tian, Yapeng Marculescu, Diana |
| contents | Recent advances in pre-trained vision transformers have shown promise in parameter-efficient audio-visual learning without audio pre-training. However, few studies have investigated effective methods for aligning multimodal features in parameter-efficient audio-visual transformers. In this paper, we propose MA-AVT, a new parameter-efficient audio-visual transformer employing deep modality alignment for corresponding multimodal semantic features. Specifically, we introduce joint unimodal and multimodal token learning for aligning the two modalities with a frozen modality-shared transformer. This allows the model to learn separate representations for each modality, while also attending to the cross-modal relationships between them. In addition, unlike prior work that only aligns coarse features from the output of unimodal encoders, we introduce blockwise contrastive learning to align coarse-to-fine-grain hierarchical features throughout the encoding phase. Furthermore, to suppress the background features in each modality from foreground matched audio-visual features, we introduce a robust discriminative foreground mining scheme. Through extensive experiments on benchmark AVE, VGGSound, and CREMA-D datasets, we achieve considerable performance improvements over SOTA methods. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_04930 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MA-AVT: Modality Alignment for Parameter-Efficient Audio-Visual Transformers Mahmud, Tanvir Mo, Shentong Tian, Yapeng Marculescu, Diana Computer Vision and Pattern Recognition Multimedia Sound Audio and Speech Processing Recent advances in pre-trained vision transformers have shown promise in parameter-efficient audio-visual learning without audio pre-training. However, few studies have investigated effective methods for aligning multimodal features in parameter-efficient audio-visual transformers. In this paper, we propose MA-AVT, a new parameter-efficient audio-visual transformer employing deep modality alignment for corresponding multimodal semantic features. Specifically, we introduce joint unimodal and multimodal token learning for aligning the two modalities with a frozen modality-shared transformer. This allows the model to learn separate representations for each modality, while also attending to the cross-modal relationships between them. In addition, unlike prior work that only aligns coarse features from the output of unimodal encoders, we introduce blockwise contrastive learning to align coarse-to-fine-grain hierarchical features throughout the encoding phase. Furthermore, to suppress the background features in each modality from foreground matched audio-visual features, we introduce a robust discriminative foreground mining scheme. Through extensive experiments on benchmark AVE, VGGSound, and CREMA-D datasets, we achieve considerable performance improvements over SOTA methods. |
| title | MA-AVT: Modality Alignment for Parameter-Efficient Audio-Visual Transformers |
| topic | Computer Vision and Pattern Recognition Multimedia Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2406.04930 |