TAViS: Text-bridged Audio-Visual Segmentation with Foundation Models

• URL: http://arxiv.org/abs/2506.11436v1
• Date: Fri, 13 Jun 2025 03:19:47 GMT
• Title: TAViS: Text-bridged Audio-Visual Segmentation with Foundation Models
• Authors: Ziyang Luo, Nian Liu, Xuguang Yang, Salman Khan, Rao Muhammad Anwer, Hisham Cholakkal, Fahad Shahbaz Khan, Junwei Han,
• Abstract summary: We present TAViS, a novel framework that textbfcouples the knowledge of multimodal foundation models for cross-modal alignment.<n> effectively combining these models poses two key challenges: the difficulty in transferring the knowledge between SAM2 and ImageBind due to their different feature spaces, and the insufficiency of using only segmentation loss for supervision.<n>Our approach achieves superior performance on single-source, multi-source, semantic datasets, and excels in zero-shot settings.
• Score: 123.17643568298116
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Audio-Visual Segmentation (AVS) faces a fundamental challenge of effectively aligning audio and visual modalities. While recent approaches leverage foundation models to address data scarcity, they often rely on single-modality knowledge or combine foundation models in an off-the-shelf manner, failing to address the cross-modal alignment challenge. In this paper, we present TAViS, a novel framework that \textbf{couples} the knowledge of multimodal foundation models (ImageBind) for cross-modal alignment and a segmentation foundation model (SAM2) for precise segmentation. However, effectively combining these models poses two key challenges: the difficulty in transferring the knowledge between SAM2 and ImageBind due to their different feature spaces, and the insufficiency of using only segmentation loss for supervision. To address these challenges, we introduce a text-bridged design with two key components: (1) a text-bridged hybrid prompting mechanism where pseudo text provides class prototype information while retaining modality-specific details from both audio and visual inputs, and (2) an alignment supervision strategy that leverages text as a bridge to align shared semantic concepts within audio-visual modalities. Our approach achieves superior performance on single-source, multi-source, semantic datasets, and excels in zero-shot settings.

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