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.
@article{arxiv.2506.06537,
title = {Bridging Audio and Vision: Zero-Shot Audiovisual Segmentation by Connecting Pretrained Models},
author = {Seung-jae Lee and Paul Hongsuck Seo},
journal= {arXiv preprint arXiv:2506.06537},
year = {2025}
}