Bridging Audio and Vision: Zero-Shot Audiovisual Segmentation by Connecting Pretrained Models

• URL: http://arxiv.org/abs/2506.06537v1
• Date: Fri, 06 Jun 2025 21:06:35 GMT
• Title: Bridging Audio and Vision: Zero-Shot Audiovisual Segmentation by Connecting Pretrained Models
• Authors: Seung-jae Lee, Paul Hongsuck Seo,
• Abstract summary: We propose a novel zero-shot AVS framework that eliminates task-specific training by leveraging multiple pretrained models.<n>Our approach integrates audio, vision, and text representations to bridge modality gaps, enabling precise sound source segmentation without AVS-specific annotations.
• Score: 13.63552417613795
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Audiovisual segmentation (AVS) aims to identify visual regions corresponding to sound sources, playing a vital role in video understanding, surveillance, and human-computer interaction. Traditional AVS methods depend on large-scale pixel-level annotations, which are costly and time-consuming to obtain. To address this, we propose a novel zero-shot AVS framework that eliminates task-specific training by leveraging multiple pretrained models. Our approach integrates audio, vision, and text representations to bridge modality gaps, enabling precise sound source segmentation without AVS-specific annotations. We systematically explore different strategies for connecting pretrained models and evaluate their efficacy across multiple datasets. Experimental results demonstrate that our framework achieves state-of-the-art zero-shot AVS performance, highlighting the effectiveness of multimodal model integration for finegrained audiovisual segmentation.

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