MA-AVT: Modality Alignment for Parameter-Efficient Audio-Visual Transformers

• URL: http://arxiv.org/abs/2406.04930v1
• Date: Fri, 7 Jun 2024 13:35:44 GMT
• Title: MA-AVT: Modality Alignment for Parameter-Efficient Audio-Visual Transformers
• Authors: Tanvir Mahmud, Shentong Mo, Yapeng Tian, Diana Marculescu,
• Abstract summary: We propose MA-AVT, a new parameter-efficient audio-visual transformer employing deep modality alignment for multimodal semantic features. Specifically, we introduce joint unimodal and multimodal token learning for aligning the two modalities with a frozen modality-shared transformer. 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.
• Score: 41.54004590821323
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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