Annotation-free Audio-Visual Segmentation

The objective of Audio-Visual Segmentation (AVS) is to locate sounding objects within visual scenes by accurately predicting pixelwise segmentation masks. In this paper, we present the following contributions: (i), we propose a scalable and annotation-free pipeline for generating artificial data for the AVS task. We leverage existing image segmentation and audio datasets to draw links between category labels, image-mask pairs, and audio samples, which allows us to easily compose (image, audio, mask) triplets for training AVS models; (ii), we introduce a novel Audio-Aware Transformer (AuTR) architecture that features an audio-aware query-based transformer decoder. This architecture enables the model to search for sounding objects with the guidance of audio signals, resulting in more accurate segmentation; (iii), we present extensive experiments conducted on both synthetic and real datasets, which demonstrate the effectiveness of training AVS models with synthetic data generated by our proposed pipeline. Additionally, our proposed AuTR architecture exhibits superior performance and strong generalization ability on public benchmarks. The project page is https://jinxiang-liu.github.io/anno-free-AVS/.