跨越音频与视觉：通过连接预训练模型实现零样态音视频分割

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。