Extending Segment Anything Model into Auditory and Temporal Dimensions for Audio-Visual Segmentation

TL;DR

This paper extends the Segment Anything Model to handle audio-visual data by incorporating spatio-temporal attention, enabling better segmentation of sound sources across video sequences and outperforming existing methods.

Contribution

The authors introduce a novel Spatio-Temporal, Bidirectional Audio-Visual Attention module into SAM, allowing it to utilize temporal context for improved audio-visual segmentation.

Findings

Achieved 8.3% mIoU improvement on AVS benchmarks.

Outperformed state-of-the-art methods in multi-source audio-visual segmentation.

Demonstrated effective utilization of spatio-temporal cross-modal relationships.

Abstract

Audio-visual segmentation (AVS) aims to segment sound sources in the video sequence, requiring a pixel-level understanding of audio-visual correspondence. As the Segment Anything Model (SAM) has strongly impacted extensive fields of dense prediction problems, prior works have investigated the introduction of SAM into AVS with audio as a new modality of the prompt. Nevertheless, constrained by SAM's single-frame segmentation scheme, the temporal context across multiple frames of audio-visual data remains insufficiently utilized. To this end, we study the extension of SAM's capabilities to the sequence of audio-visual scenes by analyzing contextual cross-modal relationships across the frames. To achieve this, we propose a Spatio-Temporal, Bidirectional Audio-Visual Attention (ST-BAVA) module integrated into the middle of SAM's image encoder and mask decoder. It adaptively updates the audio-visual features to convey the spatio-temporal correspondence between the video frames and audio streams. Extensive experiments demonstrate that our proposed model outperforms the state-of-the-art methods on AVS benchmarks, especially with an 8.3% mIoU gain on a challenging multi-sources subset.