# Improving Sound Source Localization with Joint Slot Attention on Image and Audio

arXiv: https://arxiv.org/abs/2504.15118 · HTML: https://mlsanity.com/paper/2504.15118/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 39 references that are in the map.
- First posted on arXiv 21 Apr 2025.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Object-Centric Learning with Slot Attention](https://mlsanity.com/paper/2006.15055/) (2020)
- [VGGSound: A Large-scale Audio-Visual Dataset](https://mlsanity.com/paper/2004.14368/) (2020)
- [Objects that Sound](https://mlsanity.com/paper/1712.06651/) (2017)
- [SoundNet: Learning Sound Representations from Unlabeled Video](https://mlsanity.com/paper/1610.09001/) (2016)
- [Learning to Localize Sound Source in Visual Scenes](https://mlsanity.com/paper/1803.03849/) (2018)
- [Self-Supervised Learning of Audio-Visual Objects from Video](https://mlsanity.com/paper/2008.04237/) (2020)
- [Localizing Visual Sounds the Hard Way](https://mlsanity.com/paper/2104.02691/) (2021)
- [Audio-Visual Segmentation](https://mlsanity.com/paper/2207.05042/) (2022)
- [Multiple Sound Sources Localization from Coarse to Fine](https://mlsanity.com/paper/2007.06355/) (2020)
- [Localizing Visual Sounds the Easy Way](https://mlsanity.com/paper/2203.09324/) (2022)
- [A Closer Look at Weakly-Supervised Audio-Visual Source Localization](https://mlsanity.com/paper/2209.09634/) (2022)
- [Audio-Visual Grouping Network for Sound Localization from Mixtures](https://mlsanity.com/paper/2303.17056/) (2023)
- [Learning Audio-Visual Source Localization via False Negative Aware Contrastive Learning](https://mlsanity.com/paper/2303.11302/) (2023)
- [Exploiting Transformation Invariance and Equivariance for Self-supervised Sound Localisation](https://mlsanity.com/paper/2206.12772/) (2022)
- [Learning Sound Localization Better From Semantically Similar Samples](https://mlsanity.com/paper/2202.03007/) (2022)
- [Audio-Visual Segmentation by Exploring Cross-Modal Mutual Semantics](https://mlsanity.com/paper/2307.16620/) (2023)
- [Self-Supervised Predictive Learning: A Negative-Free Method for Sound Source Localization in Visual Scenes](https://mlsanity.com/paper/2203.13412/) (2022)
- [Sound Source Localization is All about Cross-Modal Alignment](https://mlsanity.com/paper/2309.10724/) (2023)
- [T-VSL: Text-Guided Visual Sound Source Localization in Mixtures](https://mlsanity.com/paper/2404.01751/) (2024)
- [Hear The Flow: Optical Flow-Based Self-Supervised Visual Sound Source Localization](https://mlsanity.com/paper/2211.03019/) (2022)
- [Can CLIP Help Sound Source Localization?](https://mlsanity.com/paper/2311.04066/) (2023)
- [Learning to Visually Localize Sound Sources from Mixtures without Prior Source Knowledge](https://mlsanity.com/paper/2403.17420/) (2024)
- [Real-Time Acoustic Perception for Automotive Applications](https://mlsanity.com/paper/2301.12808/) (2023)

## Built on by (most cited first, 4 of 4)

- [Learning Object-Centric Representations in SAR Images with Multi-Level Feature Fusion](https://mlsanity.com/paper/2509.09298/) (2025)
- [Segmenting Collision Sound Sources in Egocentric Videos](https://mlsanity.com/paper/2511.13863/) (2025)
- [Binaural Audio-Visual Instance Segmentation](https://mlsanity.com/paper/2609.32180/) (2026)
- [SlotAVS: Object-Centric Audio-Visual Segmentation via Bi-Modal Slot Attention](https://mlsanity.com/paper/2610.15764/) (2026)

## Abstract (arXiv)

Sound source localization (SSL) is the task of locating the source of sound within an image. Due to the lack of localization labels, the de facto standard in SSL has been to represent an image and audio as a single embedding vector each, and use them to learn SSL via contrastive learning. To this end, previous work samples one of local image features as the image embedding and aggregates all local audio features to obtain the audio embedding, which is far from optimal due to the presence of noise and background irrelevant to the actual target in the input. We present a novel SSL method that addresses this chronic issue by joint slot attention on image and audio. To be specific, two slots competitively attend image and audio features to decompose them into target and off-target representations, and only target representations of image and audio are used for contrastive learning. Also, we introduce cross-modal attention matching to further align local features of image and audio. Our method achieved the best in almost all settings on three public benchmarks for SSL, and substantially outperformed all the prior work in cross-modal retrieval.