AVS-Mamba: Exploring Temporal and Multi-modal Mamba for Audio-Visual Segmentation

• URL: http://arxiv.org/abs/2501.07810v1
• Date: Tue, 14 Jan 2025 03:20:20 GMT
• Title: AVS-Mamba: Exploring Temporal and Multi-modal Mamba for Audio-Visual Segmentation
• Authors: Sitong Gong, Yunzhi Zhuge, Lu Zhang, Yifan Wang, Pingping Zhang, Lijun Wang, Huchuan Lu,
• Abstract summary: We introduce AVS-Mamba, a selective state space model to address the audio-visual segmentation task.<n>Our framework incorporates two key components for video understanding and cross-modal learning.<n>Our approach achieves new state-of-the-art results on the AVSBench-object and AVS-semantic datasets.
• Score: 62.682428307810525
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The essence of audio-visual segmentation (AVS) lies in locating and delineating sound-emitting objects within a video stream. While Transformer-based methods have shown promise, their handling of long-range dependencies struggles due to quadratic computational costs, presenting a bottleneck in complex scenarios. To overcome this limitation and facilitate complex multi-modal comprehension with linear complexity, we introduce AVS-Mamba, a selective state space model to address the AVS task. Our framework incorporates two key components for video understanding and cross-modal learning: Temporal Mamba Block for sequential video processing and Vision-to-Audio Fusion Block for advanced audio-vision integration. Building on this, we develop the Multi-scale Temporal Encoder, aimed at enhancing the learning of visual features across scales, facilitating the perception of intra- and inter-frame information. To perform multi-modal fusion, we propose the Modality Aggregation Decoder, leveraging the Vision-to-Audio Fusion Block to integrate visual features into audio features across both frame and temporal levels. Further, we adopt the Contextual Integration Pyramid to perform audio-to-vision spatial-temporal context collaboration. Through these innovative contributions, our approach achieves new state-of-the-art results on the AVSBench-object and AVSBench-semantic datasets. Our source code and model weights are available at AVS-Mamba.

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