# Self-Supervised Radio-Visual Representation Learning for 6G Sensing

arXiv: https://arxiv.org/abs/2111.02887 · HTML: https://mlsanity.com/paper/2111.02887/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 35 references that are in the map.
- First posted on arXiv 1 Nov 2021.

## Builds on (most cited first, 25 of 25)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [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)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [Colorful Image Colorization](https://mlsanity.com/paper/1603.08511/) (2016)
- [Unsupervised Visual Representation Learning by Context Prediction](https://mlsanity.com/paper/1505.05192/) (2015)
- [Data-Efficient Image Recognition with Contrastive Predictive Coding](https://mlsanity.com/paper/1905.09272/) (2019)
- [On Variational Bounds of Mutual Information](https://mlsanity.com/paper/1905.06922/) (2019)
- [Look, Listen and Learn](https://mlsanity.com/paper/1705.08168/) (2017)
- [Objects that Sound](https://mlsanity.com/paper/1712.06651/) (2017)
- [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)
- [RODNet: A Real-Time Radar Object Detection Network Cross-Supervised by Camera-Radar Fused Object 3D Localization](https://mlsanity.com/paper/2102.05150/) (2021)
- [R4Dyn: Exploring Radar for Self-Supervised Monocular Depth Estimation of Dynamic Scenes](https://mlsanity.com/paper/2108.04814/) (2021)
- [6G White Paper on Localization and Sensing](https://mlsanity.com/paper/2006.01779/) (2020)
- [Proxy-Normalizing Activations to Match Batch Normalization while Removing Batch Dependence](https://mlsanity.com/paper/2106.03743/) (2021)
- [Coherent, super resolved radar beamforming using self-supervised learning](https://mlsanity.com/paper/2106.13085/) (2021)
- [Simplified Ray Tracing for the Millimeter Wave Channel: A Performance Evaluation](https://mlsanity.com/paper/2002.09179/) (2020)
- [Deep Inertial Navigation using Continuous Domain Adaptation and Optimal Transport](https://mlsanity.com/paper/2106.15178/) (2021)

## Built on by (most cited first, 6 of 6)

- [Bootstrapping Autonomous Driving Radars with Self-Supervised Learning](https://mlsanity.com/paper/2312.04519/) (2023)
- [Look, Radiate, and Learn: Self-Supervised Localisation via Radio-Visual Correspondence](https://mlsanity.com/paper/2206.06424/) (2022)
- [ViFiCon: Vision and Wireless Association Via Self-Supervised Contrastive Learning](https://mlsanity.com/paper/2210.05513/) (2022)
- [RADLER: Radar Object Detection Leveraging Semantic 3D City Models and Self-Supervised Radar-Image Learning](https://mlsanity.com/paper/2504.12167/) (2025)
- [Equivariant Multi-agent Reinforcement Learning for Multimodal Vehicle-to-Infrastructure Systems](https://mlsanity.com/paper/2604.06914/) (2026)
- [Encoder-Decoder Networks for Self-Supervised Pretraining and Downstream Signal Bandwidth Regression on Digital Antenna Arrays](https://mlsanity.com/paper/2307.03327/) (2023)

## Abstract (arXiv)

In future 6G cellular networks, a joint communication and sensing protocol will allow the network to perceive the environment, opening the door for many new applications atop a unified communication-perception infrastructure. However, interpreting the sparse radio representation of sensing scenes is challenging, which hinders the potential of these emergent systems. We propose to combine radio and vision to automatically learn a radio-only sensing model with minimal human intervention. We want to build a radio sensing model that can feed on millions of uncurated data points. To this end, we leverage recent advances in self-supervised learning and formulate a new label-free radio-visual co-learning scheme, whereby vision trains radio via cross-modal mutual information. We implement and evaluate our scheme according to the common linear classification benchmark, and report qualitative and quantitative performance metrics. In our evaluation, the representation learnt by radio-visual self-supervision works well for a downstream sensing demonstrator, and outperforms its fully-supervised counterpart when less labelled data is used. This indicates that self-supervised learning could be an important enabler for future scalable radio sensing systems.