# RF-Net: a Unified Meta-learning Framework for RF-enabled One-shot Human Activity Recognition

arXiv: https://arxiv.org/abs/2111.04566 · HTML: https://mlsanity.com/paper/2111.04566/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 83 references that are in the map.
- First posted on arXiv 29 Oct 2021.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [TSM: Temporal Shift Module for Efficient Video Understanding](https://mlsanity.com/paper/1811.08383/) (2018)
- [A Closer Look at Few-shot Classification](https://mlsanity.com/paper/1904.04232/) (2019)
- [Meta-Learning with Latent Embedding Optimization](https://mlsanity.com/paper/1807.05960/) (2018)
- [Meta-SGD: Learning to Learn Quickly for Few-Shot Learning](https://mlsanity.com/paper/1707.09835/) (2017)
- [Meta-Transfer Learning for Few-Shot Learning](https://mlsanity.com/paper/1812.02391/) (2018)
- [Bilinear Attention Networks](https://mlsanity.com/paper/1805.07932/) (2018)
- [Meta Networks](https://mlsanity.com/paper/1703.00837/) (2017)
- [Task-Agnostic Meta-Learning for Few-shot Learning](https://mlsanity.com/paper/1805.07722/) (2018)
- [Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach](https://mlsanity.com/paper/1704.02447/) (2017)
- [Benchmark Analysis of Representative Deep Neural Network Architectures](https://mlsanity.com/paper/1810.00736/) (2018)
- [Going Deeper into First-Person Activity Recognition](https://mlsanity.com/paper/1605.03688/) (2016)
- [Multi-modality Sensor Data Classification with Selective Attention](https://mlsanity.com/paper/1804.05493/) (2018)
- [V2iFi: in-Vehicle Vital Sign Monitoring via Compact RF Sensing](https://mlsanity.com/paper/2110.14848/) (2021)
- [MilliSonic: Pushing the Limits of Acoustic Motion Tracking](https://mlsanity.com/paper/1901.06601/) (2019)

## Built on by (most cited first, 24 of 24)

- [AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving](https://mlsanity.com/paper/2302.08646/) (2023)
- [EdgeFM: Leveraging Foundation Model for Open-set Learning on the Edge](https://mlsanity.com/paper/2311.10986/) (2023)
- [Towards Domain-Independent and Real-Time Gesture Recognition Using mmWave Signal](https://mlsanity.com/paper/2111.06195/) (2021)
- [WiMANS: A Benchmark Dataset for WiFi-based Multi-user Activity Sensing](https://mlsanity.com/paper/2402.09430/) (2024)
- [MoRe-Fi: Motion-robust and Fine-grained Respiration Monitoring via Deep-Learning UWB Radar](https://mlsanity.com/paper/2111.08195/) (2021)
- [LifeLearner: Hardware-Aware Meta Continual Learning System for Embedded Computing Platforms](https://mlsanity.com/paper/2311.11420/) (2023)
- [RFBoost: Understanding and Boosting Deep WiFi Sensing via Physical Data Augmentation](https://mlsanity.com/paper/2410.07230/) (2024)
- [A Survey on Wi-Fi Sensing Generalizability: Taxonomy, Techniques, Datasets, and Future Research Prospects](https://mlsanity.com/paper/2503.08008/) (2025)
- [Exposing the CSI: A Systematic Investigation of CSI-based Wi-Fi Sensing Capabilities and Limitations](https://mlsanity.com/paper/2302.00992/) (2023)
- [Large Model for Small Data: Foundation Model for Cross-Modal RF Human Activity Recognition](https://mlsanity.com/paper/2410.19766/) (2024)
- [From Orthogonality to Dependency: Learning Disentangled Representation for Multi-Modal Time-Series Sensing Signals](https://mlsanity.com/paper/2405.16083/) (2024)
- [GesturePrint: Enabling User Identification for mmWave-based Gesture Recognition Systems](https://mlsanity.com/paper/2408.05358/) (2024)
- [WiFlexFormer: Efficient WiFi-Based Person-Centric Sensing](https://mlsanity.com/paper/2411.04224/) (2024)
- [OCHID-Fi: Occlusion-Robust Hand Pose Estimation in 3D via RF-Vision](https://mlsanity.com/paper/2308.10146/) (2023)
- [Accurate Passive Radar via an Uncertainty-Aware Fusion of Wi-Fi Sensing Data](https://mlsanity.com/paper/2407.04733/) (2024)
- [Neuro-Symbolic Fusion of Wi-Fi Sensing Data for Passive Radar with Inter-Modal Knowledge Transfer](https://mlsanity.com/paper/2407.04734/) (2024)
- [DATTA: Domain-Adversarial Test-Time Adaptation for Cross-Domain WiFi-Based Human Activity Recognition](https://mlsanity.com/paper/2411.13284/) (2024)
- [NeurIT: Pushing the Limit of Neural Inertial Tracking for Indoor Robotic IoT](https://mlsanity.com/paper/2404.08939/) (2024)
- [GenHPE: Generative Counterfactuals for 3D Human Pose Estimation with Radio Frequency Signals](https://mlsanity.com/paper/2503.09537/) (2025)
- [Towards White-Box Deep Wireless Sensing](https://mlsanity.com/paper/2507.21799/) (2025)
- [maxVSTAR: Maximally Adaptive Vision-Guided CSI Sensing with Closed-Loop Edge Model Adaptation for Robust Human Activity Recognition](https://mlsanity.com/paper/2510.26146/) (2025)
- [RF-LEGO: Modularized Signal Processing-Deep Learning Co-Design for RF Sensing via Deep Unrolling](https://mlsanity.com/paper/2604.10183/) (2026)
- [RePos: Relative-to-Absolute Pose Factorization for Cross-Environment WiFi-Based 3D Human Pose Estimation](https://mlsanity.com/paper/2607.02986/) (2026)
- [The Universal Language of CSI:Unifying Wireless Sensing Across Devices and Environments](https://mlsanity.com/paper/2607.09727/) (2026)

## Abstract (arXiv)

Radio-Frequency (RF) based device-free Human Activity Recognition (HAR) rises as a promising solution for many applications. However, device-free (or contactless) sensing is often more sensitive to environment changes than device-based (or wearable) sensing. Also, RF datasets strictly require on-line labeling during collection, starkly different from image and text data collections where human interpretations can be leveraged to perform off-line labeling. Therefore, existing solutions to RF-HAR entail a laborious data collection process for adapting to new environments. To this end, we propose RF-Net as a meta-learning based approach to one-shot RF-HAR; it reduces the labeling efforts for environment adaptation to the minimum level. In particular, we first examine three representative RF sensing techniques and two major meta-learning approaches. The results motivate us to innovate in two designs: i) a dual-path base HAR network, where both time and frequency domains are dedicated to learning powerful RF features including spatial and attention-based temporal ones, and ii) a metric-based meta-learning framework to enhance the fast adaption capability of the base network, including an RF-specific metric module along with a residual classification module. We conduct extensive experiments based on all three RF sensing techniques in multiple real-world indoor environments; all results strongly demonstrate the efficacy of RF-Net compared with state-of-the-art baselines.