# Talk is Not Always Cheap: Promoting Wireless Sensing Models with Text Prompts

arXiv: https://arxiv.org/abs/2504.14621 · HTML: https://mlsanity.com/paper/2504.14621/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 48 references that are in the map.
- First posted on arXiv 20 Apr 2025.

## Builds on (most cited first, 13 of 13)

- [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://mlsanity.com/paper/2308.03281/) (2023)
- [TriDet: Temporal Action Detection with Relative Boundary Modeling](https://mlsanity.com/paper/2303.07347/) (2023)
- [Zero-Shot Temporal Action Detection via Vision-Language Prompting](https://mlsanity.com/paper/2207.08184/) (2022)
- [Person-in-WiFi: Fine-grained Person Perception using WiFi](https://mlsanity.com/paper/1904.00276/) (2019)
- [Action-GPT: Leveraging Large-scale Language Models for Improved and Generalized Action Generation](https://mlsanity.com/paper/2211.15603/) (2022)
- [XRF V2: A Dataset for Action Summarization with Wi-Fi Signals, and IMUs in Phones, Watches, Earbuds, and Glasses](https://mlsanity.com/paper/2501.19034/) (2025)
- [A Survey on Wi-Fi Sensing Generalizability: Taxonomy, Techniques, Datasets, and Future Research Prospects](https://mlsanity.com/paper/2503.08008/) (2025)
- [ActionPrompt: Action-Guided 3D Human Pose Estimation With Text and Pose Prompting](https://mlsanity.com/paper/2307.09026/) (2023)
- [Large Model for Small Data: Foundation Model for Cross-Modal RF Human Activity Recognition](https://mlsanity.com/paper/2410.19766/) (2024)
- [WiFi CSI Based Temporal Activity Detection via Dual Pyramid Network](https://mlsanity.com/paper/2412.16233/) (2024)
- [Wi-Chat: Large Language Model Powered Wi-Fi Sensing](https://mlsanity.com/paper/2502.12421/) (2025)
- [PALMAR: Towards Adaptive Multi-inhabitant Activity Recognition in Point-Cloud Technology](https://mlsanity.com/paper/2106.11902/) (2021)
- [mmEgoHand: Egocentric Hand Pose Estimation and Gesture Recognition with Head-mounted Millimeter-wave Radar and IMU](https://mlsanity.com/paper/2501.13805/) (2025)

## Built on by (most cited first, 1 of 1)

- [FSTC-Encoder: Feature--Spatial--Temporal Correlation Learning for Generalizable RF Sensing](https://mlsanity.com/paper/2608.08439/) (2026)

## Abstract (arXiv)

Wireless signal-based human sensing technologies, such as WiFi, millimeter-wave (mmWave) radar, and Radio Frequency Identification (RFID), enable the detection and interpretation of human presence, posture, and activities, thereby providing critical support for applications in public security, healthcare, and smart environments. These technologies exhibit notable advantages due to their non-contact operation and environmental adaptability; however, existing systems often fail to leverage the textual information inherent in datasets. To address this, we propose an innovative text-enhanced wireless sensing framework, WiTalk, that seamlessly integrates semantic knowledge through three hierarchical prompt strategies-label-only, brief description, and detailed action description-without requiring architectural modifications or incurring additional data costs. We rigorously validate this framework across three public benchmark datasets: XRF55 for human action recognition (HAR), and WiFiTAL and XRFV2 for WiFi temporal action localization (TAL). Experimental results demonstrate significant performance improvements: on XRF55, accuracy for WiFi, RFID, and mmWave increases by 3.9%, 2.59%, and 0.46%, respectively; on WiFiTAL, the average performance of WiFiTAD improves by 4.98%; and on XRFV2, the mean average precision gains across various methods range from 4.02% to 13.68%. Our codes have been included in https://github.com/yangzhenkui/WiTalk.