# WiFlexFormer: Efficient WiFi-Based Person-Centric Sensing

arXiv: https://arxiv.org/abs/2411.04224 · HTML: https://mlsanity.com/paper/2411.04224/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 6 Nov 2024.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [Diversity with Cooperation: Ensemble Methods for Few-Shot Classification](https://mlsanity.com/paper/1903.11341/) (2019)
- [RF-Net: a Unified Meta-learning Framework for RF-enabled One-shot Human Activity Recognition](https://mlsanity.com/paper/2111.04566/) (2021)
- [MetaFi: Device-Free Pose Estimation via Commodity WiFi for Metaverse Avatar Simulation](https://mlsanity.com/paper/2208.10414/) (2022)
- [RFBoost: Understanding and Boosting Deep WiFi Sensing via Physical Data Augmentation](https://mlsanity.com/paper/2410.07230/) (2024)

## Built on by (most cited first, 3 of 3)

- [DATTA: Domain-Adversarial Test-Time Adaptation for Cross-Domain WiFi-Based Human Activity Recognition](https://mlsanity.com/paper/2411.13284/) (2024)
- [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)
- [UniCSI Towards a Universal Wi-Fi CSI Encoder for Ubiquitous Human Sensing](https://mlsanity.com/paper/2610.09559/) (2026)

## Abstract (arXiv)

We propose WiFlexFormer, a highly efficient Transformer-based architecture designed for WiFi Channel State Information (CSI)-based person-centric sensing. We benchmark WiFlexFormer against state-of-the-art vision and specialized architectures for processing radio frequency data and demonstrate that it achieves comparable Human Activity Recognition (HAR) performance while offering a significantly lower parameter count and faster inference times. With an inference time of just 10 ms on an Nvidia Jetson Orin Nano, WiFlexFormer is optimized for real-time inference. Additionally, its low parameter count contributes to improved cross-domain generalization, where it often outperforms larger models. Our comprehensive evaluation shows that WiFlexFormer is a potential solution for efficient, scalable WiFi-based sensing applications. The PyTorch implementation of WiFlexFormer is publicly available at: https://github.com/StrohmayerJ/WiFlexFormer.