# Selective Noise Suppression and Discriminative Mutual Interaction for Robust Audio-Visual Segmentation

arXiv: https://arxiv.org/abs/2603.14203 · HTML: https://mlsanity.com/paper/2603.14203/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 51 references that are in the map.
- First posted on arXiv 15 Mar 2026.

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

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [Look, Listen and Learn](https://mlsanity.com/paper/1705.08168/) (2017)
- [Attention Bottlenecks for Multimodal Fusion](https://mlsanity.com/paper/2107.00135/) (2021)
- [Learning to Localize Sound Source in Visual Scenes](https://mlsanity.com/paper/1803.03849/) (2018)
- [Audio-Visual Segmentation](https://mlsanity.com/paper/2207.05042/) (2022)
- [Visually Indicated Sounds](https://mlsanity.com/paper/1512.08512/) (2015)
- [Learning Representations from Audio-Visual Spatial Alignment](https://mlsanity.com/paper/2011.01819/) (2020)
- [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)
- [Referred by Multi-Modality: A Unified Temporal Transformer for Video Object Segmentation](https://mlsanity.com/paper/2305.16318/) (2023)
- [CATR: Combinatorial-Dependence Audio-Queried Transformer for Audio-Visual Video Segmentation](https://mlsanity.com/paper/2309.09709/) (2023)
- [Annotation-free Audio-Visual Segmentation](https://mlsanity.com/paper/2305.11019/) (2023)
- [Improving Audio-Visual Segmentation with Bidirectional Generation](https://mlsanity.com/paper/2308.08288/) (2023)
- [Audio-aware Query-enhanced Transformer for Audio-Visual Segmentation](https://mlsanity.com/paper/2307.13236/) (2023)
- [Ref-AVS: Refer and Segment Objects in Audio-Visual Scenes](https://mlsanity.com/paper/2407.10957/) (2024)
- [Audio-Visual Segmentation by Exploring Cross-Modal Mutual Semantics](https://mlsanity.com/paper/2307.16620/) (2023)
- [BAVS: Bootstrapping Audio-Visual Segmentation by Integrating Foundation Knowledge](https://mlsanity.com/paper/2308.10175/) (2023)
- [Discovering Sounding Objects by Audio Queries for Audio Visual Segmentation](https://mlsanity.com/paper/2309.09501/) (2023)
- [Cooperation Does Matter: Exploring Multi-Order Bilateral Relations for Audio-Visual Segmentation](https://mlsanity.com/paper/2312.06462/) (2023)
- [Stepping Stones: A Progressive Training Strategy for Audio-Visual Semantic Segmentation](https://mlsanity.com/paper/2407.11820/) (2024)
- [Audio-Visual Instance Segmentation](https://mlsanity.com/paper/2310.18709/) (2023)
- [QDFormer: Towards Robust Audiovisual Segmentation in Complex Environments with Quantization-based Semantic Decomposition](https://mlsanity.com/paper/2310.00132/) (2023)
- [AVS-Mamba: Exploring Temporal and Multi-modal Mamba for Audio-Visual Segmentation](https://mlsanity.com/paper/2501.07810/) (2025)
- [Cross-modal Cognitive Consensus guided Audio-Visual Segmentation](https://mlsanity.com/paper/2310.06259/) (2023)
- [Unveiling and Mitigating Bias in Audio Visual Segmentation](https://mlsanity.com/paper/2407.16638/) (2024)
- [Extending Segment Anything Model into Auditory and Temporal Dimensions for Audio-Visual Segmentation](https://mlsanity.com/paper/2406.06163/) (2024)
- [Audio-visual scene classification via contrastive event-object alignment and semantic-based fusion](https://mlsanity.com/paper/2208.02086/) (2022)

## Built on by (most cited first, 1 of 1)

- [Hear to See: Discerning Stateful Listening for Audio-Visual Instance Segmentation](https://mlsanity.com/paper/2608.03264/) (2026)

## Abstract (arXiv)

The ability to capture and segment sounding objects in dynamic visual scenes is crucial for the development of Audio-Visual Segmentation (AVS) tasks. While significant progress has been made in this area, the interaction between audio and visual modalities still requires further exploration. In this work, we aim to answer the following questions: How can a model effectively suppress audio noise while enhancing relevant audio information? How can we achieve discriminative interaction between the audio and visual modalities? To this end, we propose SDAVS, equipped with the Selective Noise-Resilient Processor (SNRP) module and the Discriminative Audio-Visual Mutual Fusion (DAMF) strategy. The proposed SNRP mitigates audio noise interference by selectively emphasizing relevant auditory cues, while DAMF ensures more consistent audio-visual representations. Experimental results demonstrate that our proposed method achieves state-of-the-art performance on benchmark AVS datasets, especially in multi-source and complex scenes. textitThe code and model are available at https://github.com/happylife-pk/SDAVS.