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

arXiv: https://arxiv.org/abs/2501.07810 · HTML: https://mlsanity.com/paper/2501.07810/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Placed in Speech and audio SSMs, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 44 of its 68 references that are in the map.
- First posted on arXiv 14 Jan 2025.

## Placed in

- State-space sequence models (S4, Mamba) / [Speech and audio SSMs](https://mlsanity.com/ssm/speech-and-audio-ssms/)

## Builds on (most cited first, 30 of 44)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [VMamba: Visual State Space Model](https://mlsanity.com/paper/2401.10166/) (2024)
- [PVT v2: Improved Baselines with Pyramid Vision Transformer](https://mlsanity.com/paper/2106.13797/) (2021)
- [Look, Listen and Learn](https://mlsanity.com/paper/1705.08168/) (2017)
- [MambaIR: A Simple Baseline for Image Restoration with State-Space Model](https://mlsanity.com/paper/2402.15648/) (2024)
- [VideoMamba: State Space Model for Efficient Video Understanding](https://mlsanity.com/paper/2403.06977/) (2024)
- [LocalMamba: Visual State Space Model with Windowed Selective Scan](https://mlsanity.com/paper/2403.09338/) (2024)
- [Learning to Answer Questions in Dynamic Audio-Visual Scenarios](https://mlsanity.com/paper/2203.14072/) (2022)
- [Audio-Visual Segmentation](https://mlsanity.com/paper/2207.05042/) (2022)
- [EfficientVMamba: Atrous Selective Scan for Light Weight Visual Mamba](https://mlsanity.com/paper/2403.09977/) (2024)
- [A Tutorial on Principal Component Analysis](https://mlsanity.com/paper/1404.1100/) (2014)
- [Is Mamba Effective for Time Series Forecasting?](https://mlsanity.com/paper/2403.11144/) (2024)
- [Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understanding](https://mlsanity.com/paper/2403.09626/) (2024)
- [Cobra: Extending Mamba to Multi-Modal Large Language Model for Efficient Inference](https://mlsanity.com/paper/2403.14520/) (2024)
- [RSMamba: Remote Sensing Image Classification with State Space Model](https://mlsanity.com/paper/2403.19654/) (2024)
- [VL-Mamba: Exploring State Space Models for Multimodal Learning](https://mlsanity.com/paper/2403.13600/) (2024)
- [VmambaIR: Visual State Space Model for Image Restoration](https://mlsanity.com/paper/2403.11423/) (2024)
- [MedMamba: Vision Mamba for Medical Image Classification](https://mlsanity.com/paper/2403.03849/) (2024)
- [Audio-Visual Segmentation with Semantics](https://mlsanity.com/paper/2301.13190/) (2023)
- [AVSegFormer: Audio-Visual Segmentation with Transformer](https://mlsanity.com/paper/2307.01146/) (2023)
- [MiM-ISTD: Mamba-in-Mamba for Efficient Infrared Small Target Detection](https://mlsanity.com/paper/2403.02148/) (2024)
- [CATR: Combinatorial-Dependence Audio-Queried Transformer for Audio-Visual Video Segmentation](https://mlsanity.com/paper/2309.09709/) (2023)
- [Audio-Visual Grouping Network for Sound Localization from Mixtures](https://mlsanity.com/paper/2303.17056/) (2023)
- [VM-UNET-V2 Rethinking Vision Mamba UNet for Medical Image Segmentation](https://mlsanity.com/paper/2403.09157/) (2024)
- [Multimodal Variational Auto-encoder based Audio-Visual Segmentation](https://mlsanity.com/paper/2310.08303/) (2023)

## Built on by (most cited first, 16 of 16)

- [The Devil is in Temporal Token: High Quality Video Reasoning Segmentation](https://mlsanity.com/paper/2501.08549/) (2025)
- [Multimodal Referring Segmentation: A Survey](https://mlsanity.com/paper/2508.00265/) (2025)
- [MUG: Pseudo Labeling Augmented Audio-Visual Mamba Network for Audio-Visual Video Parsing](https://mlsanity.com/paper/2507.01384/) (2025)
- [Delayed Bidirectional Alignment via Disentangled Audio Semantics for Audio-Visual Segmentation](https://mlsanity.com/paper/2512.20117/) (2025)
- [Lightweight Quad Bayer HybridEVS Demosaicing via State Space Augmented Cross-Attention](https://mlsanity.com/paper/2508.06058/) (2025)
- [How Do Optical Flow and Textual Prompts Collaborate to Assist in Audio-Visual Semantic Segmentation?](https://mlsanity.com/paper/2601.08133/) (2026)
- [MSF-Mamba: Motion-aware State Fusion Mamba for Efficient Micro-Gesture Recognition](https://mlsanity.com/paper/2510.10478/) (2025)
- [SeaVIS: Sound-Enhanced Association for Online Audio-Visual Instance Segmentation](https://mlsanity.com/paper/2603.01431/) (2026)
- [Selective Noise Suppression and Discriminative Mutual Interaction for Robust Audio-Visual Segmentation](https://mlsanity.com/paper/2603.14203/) (2026)
- [Parameter Aware Mamba Model for Multi-task Dense Prediction](https://mlsanity.com/paper/2511.14503/) (2025)
- [AV-Unified: A Unified Framework for Audio-visual Scene Understanding](https://mlsanity.com/paper/2603.06530/) (2026)
- [LightAVSeg: Lightweight Audio-Visual Segmentation](https://mlsanity.com/paper/2605.08805/) (2026)
- [UniSkip-Mamba: A Frequency-Aware State Space Model for Audio-Visual Temporal Forgery Localization](https://mlsanity.com/paper/2607.04498/) (2026)
- [Hear to See: Discerning Stateful Listening for Audio-Visual Instance Segmentation](https://mlsanity.com/paper/2608.03264/) (2026)
- [Audio-Visual Segmentation via Depth-Guided Collaborative Modeling](https://mlsanity.com/paper/2608.16285/) (2026)
- [SlotAVS: Object-Centric Audio-Visual Segmentation via Bi-Modal Slot Attention](https://mlsanity.com/paper/2610.15764/) (2026)

## Abstract (arXiv)

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.