# Exploiting Transformation Invariance and Equivariance for Self-supervised Sound Localisation

arXiv: https://arxiv.org/abs/2206.12772 · HTML: https://mlsanity.com/paper/2206.12772/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- Builds on 47 of its 55 references that are in the map.
- First posted on arXiv 26 Jun 2022.

## Builds on (most cited first, 30 of 47)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [VGGSound: A Large-scale Audio-Visual Dataset](https://mlsanity.com/paper/2004.14368/) (2020)
- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [What Makes Training Multi-Modal Classification Networks Hard?](https://mlsanity.com/paper/1905.12681/) (2019)
- [Audio-Visual Scene Analysis with Self-Supervised Multisensory Features](https://mlsanity.com/paper/1804.03641/) (2018)
- [Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2004.04581/) (2020)
- [The Sound of Pixels](https://mlsanity.com/paper/1804.03160/) (2018)
- [Objects that Sound](https://mlsanity.com/paper/1712.06651/) (2017)
- [Cooperative Learning of Audio and Video Models from Self-Supervised Synchronization](https://mlsanity.com/paper/1807.00230/) (2018)
- [SoundNet: Learning Sound Representations from Unlabeled Video](https://mlsanity.com/paper/1610.09001/) (2016)
- [EPIC-Fusion: Audio-Visual Temporal Binding for Egocentric Action Recognition](https://mlsanity.com/paper/1908.08498/) (2019)
- [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)
- [Listen to Look: Action Recognition by Previewing Audio](https://mlsanity.com/paper/1912.04487/) (2019)
- [The Sound of Motions](https://mlsanity.com/paper/1904.05979/) (2019)
- [Audiovisual SlowFast Networks for Video Recognition](https://mlsanity.com/paper/2001.08740/) (2020)
- [Unified Multisensory Perception: Weakly-Supervised Audio-Visual Video Parsing](https://mlsanity.com/paper/2007.10558/) (2020)
- [Multiple Sound Sources Localization from Coarse to Fine](https://mlsanity.com/paper/2007.06355/) (2020)
- [Learning to Separate Object Sounds by Watching Unlabeled Video](https://mlsanity.com/paper/1804.01665/) (2018)
- [VisualVoice: Audio-Visual Speech Separation with Cross-Modal Consistency](https://mlsanity.com/paper/2101.03149/) (2021)
- [Music Gesture for Visual Sound Separation](https://mlsanity.com/paper/2004.09476/) (2020)

## Built on by (most cited first, 30 of 44)

- [Learning Audio-Visual Source Localization via False Negative Aware Contrastive Learning](https://mlsanity.com/paper/2303.11302/) (2023)
- [Annotation-free Audio-Visual Segmentation](https://mlsanity.com/paper/2305.11019/) (2023)
- [Audio-aware Query-enhanced Transformer for Audio-Visual Segmentation](https://mlsanity.com/paper/2307.13236/) (2023)
- [Audio-Visual Segmentation by Exploring Cross-Modal Mutual Semantics](https://mlsanity.com/paper/2307.16620/) (2023)
- [Sound Source Localization is All about Cross-Modal Alignment](https://mlsanity.com/paper/2309.10724/) (2023)
- [FOLDER: Accelerating Multi-modal Large Language Models with Enhanced Performance](https://mlsanity.com/paper/2501.02430/) (2025)
- [AttrSeg: Open-Vocabulary Semantic Segmentation via Attribute Decomposition-Aggregation](https://mlsanity.com/paper/2309.00096/) (2023)
- [Audio-Visual Segmentation via Unlabeled Frame Exploitation](https://mlsanity.com/paper/2403.11074/) (2024)
- [Meerkat: Audio-Visual Large Language Model for Grounding in Space and Time](https://mlsanity.com/paper/2407.01851/) (2024)
- [Seeing Sound, Hearing Sight: Uncovering Modality Bias and Conflict of AI models in Sound Localization](https://mlsanity.com/paper/2505.11217/) (2025)
- [MarginNCE: Robust Sound Localization with a Negative Margin](https://mlsanity.com/paper/2211.01966/) (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)
- [Turbo: Informativity-Driven Acceleration Plug-In for Vision-Language Large Models](https://mlsanity.com/paper/2407.11717/) (2024)
- [Advancing Myopia To Holism: Fully Contrastive Language-Image Pre-training](https://mlsanity.com/paper/2412.00440/) (2024)
- [Distilling Vision-Language Pre-training to Collaborate with Weakly-Supervised Temporal Action Localization](https://mlsanity.com/paper/2212.09335/) (2022)
- [Turbo: Informativity-Driven Acceleration Plug-In for Vision-Language Models](https://mlsanity.com/paper/2312.07408/) (2023)
- [Dual Mean-Teacher: An Unbiased Semi-Supervised Framework for Audio-Visual Source Localization](https://mlsanity.com/paper/2403.03145/) (2024)
- [DENOISER: Rethinking the Robustness for Open-Vocabulary Action Recognition](https://mlsanity.com/paper/2404.14890/) (2024)
- [Aligning Sight and Sound: Advanced Sound Source Localization Through Audio-Visual Alignment](https://mlsanity.com/paper/2407.13676/) (2024)
- [Made to Order: Discovering monotonic temporal changes via self-supervised video ordering](https://mlsanity.com/paper/2404.16828/) (2024)
- [Object-aware Sound Source Localization via Audio-Visual Scene Understanding](https://mlsanity.com/paper/2506.18557/) (2025)
- [Segment Beyond View: Handling Partially Missing Modality for Audio-Visual Semantic Segmentation](https://mlsanity.com/paper/2312.08673/) (2023)
- [SAVE: Segment Audio-Visual Easy way using Segment Anything Model](https://mlsanity.com/paper/2407.02004/) (2024)
- [A Critical Assessment of Visual Sound Source Localization Models Including Negative Audio](https://mlsanity.com/paper/2410.01020/) (2024)
- [Contrast-Unity for Partially-Supervised Temporal Sentence Grounding](https://mlsanity.com/paper/2502.12917/) (2025)
- [Improving Sound Source Localization with Joint Slot Attention on Image and Audio](https://mlsanity.com/paper/2504.15118/) (2025)
- [What's Making That Sound Right Now? Video-centric Audio-Visual Localization](https://mlsanity.com/paper/2507.04667/) (2025)
- [Cross Pseudo-Labeling for Semi-Supervised Audio-Visual Source Localization](https://mlsanity.com/paper/2403.03095/) (2024)
- [Enhancing Sound Source Localization via False Negative Elimination](https://mlsanity.com/paper/2408.16448/) (2024)

## Abstract (arXiv)

We present a simple yet effective self-supervised framework for audio-visual representation learning, to localize the sound source in videos. To understand what enables to learn useful representations, we systematically investigate the effects of data augmentations, and reveal that (1) composition of data augmentations plays a critical role, i.e. explicitly encouraging the audio-visual representations to be invariant to various transformations~(em transformation invariance); (2) enforcing geometric consistency substantially improves the quality of learned representations, i.e. the detected sound source should follow the same transformation applied on input video frames~(em transformation equivariance). Extensive experiments demonstrate that our model significantly outperforms previous methods on two sound localization benchmarks, namely, Flickr-SoundNet and VGG-Sound. Additionally, we also evaluate audio retrieval and cross-modal retrieval tasks. In both cases, our self-supervised models demonstrate superior retrieval performances, even competitive with the supervised approach in audio retrieval. This reveals the proposed framework learns strong multi-modal representations that are beneficial to sound localisation and generalization to further applications. textitAll codes will be available.