arXiv Computer Vision

CIG-MAE: Cross-Modal Information-Guided Masked Autoencoder for Self-Supervised WiFi Sensing

CIG-MAE is a self‑supervised framework for WiFi‑based human action recognition that uses a cross‑modal masked autoencoder to reconstruct both amplitude and phase of Channel State Information. It introduces an adaptive, information‑guided masking strategy that focuses on high‑density time‑frequency regions and employs a Barlow Twins regularizer to align cross‑modal representations without negative samples. Experiments on three public datasets show that CIG‑MAE outperforms state‑of‑the‑art SSL methods and even surpasses a fully supervised baseline, highlighting its data efficiency, robustness, and generalization.

arXiv Machine Learning

1d ago

CSI-JEPA: Towards Foundation Representations for Ubiquitous Wi-Fi Sensing with Minimal Supervision

CSI-JEPA is a self‑supervised framework that learns reusable temporal‑spectral representations from unlabeled Wi‑Fi channel state information (CSI) by predicting latent features of masked channel regions. It tokenizes CSI along time and subcarrier dimensions and uses a channel‑variation‑aware masking strategy to focus on dynamic regions. After pretraining, the frozen encoder is adapted with lightweight adapters to a variety of downstream Wi‑Fi sensing tasks, achieving significant accuracy gains and label savings compared to supervised baselines.

By Xuanhao Luo, Zhizhen Li, Yuchen Liu

llms

arXiv AI

Jul 14

The Universal Language of CSI:Unifying Wireless Sensing Across Devices and Environments

arXiv:2607. 09727v1 Announce Type: cross Abstract: WiFi sensing based on Channel State Information (CSI) promises ubiquitous, device-free perception, yet current research remains trapped in a Tower of Babel - fragmented into isolated silos where models are tailored to specific hardware dialects, fixed environments, and narrow tasks.

By Jiayi Chen, Weiting Ou, Guangxu Zhu

llms

arXiv AI

Aug 18

Federated Self-Supervised Modulation Classification under Non-IID and Imbalanced Data

arXiv:2510. 04927v2 Announce Type: replace-cross Abstract: Automatic modulation classification (AMC) is a core enabler of cognitive wireless systems, providing spectrum awareness and supporting adaptive communication at the network edge.

By Usman Akram, Yiyue Chen, Haris Vikalo

safety

arXiv AI

Jun 2

Physics-Guided Attention in a Lightweight TCN for Efficient WiFi CSI-Based Human Activity Recognition

arXiv:2606. 01834v1 Announce Type: cross Abstract: Human Action Recognition (HAR) using WiFi Channel State Information (CSI) has gained increasing attention due to its non-contact, low-cost, and privacy-preserving nature.

By Chinthaka Ranasingha, Tharindu Fernando, Sridha Sridharan, Clinton Fookes, Harshala Gammulle

benchmarks safety

arXiv Machine Learning

Oct 8

UniCSI Towards a Universal Wi-Fi CSI Encoder for Ubiquitous Human Sensing

arXiv:2610.09559v1 Announce Type: new Abstract: Wi-Fi sensing promises to turn the everyday wireless signals that already surround us into ubiquitous sensors for human sensing. However, a fundamental...

By Daniel Eckhoff, Hua Kang, Zhitang Chen, Jie Chuai

nlp

arXiv Machine Learning

Oct 15

W-JEPA: Wavelet Joint-Embedding Predictive Architecture for Wearable Activity Recognition

arXiv:2610.19411v1 Announce Type: new Abstract: A major challenge in wearable device activity recognition is the heterogeneity across datasets in sensor placement, channel count, sampling rate and su...

By Yogesh Prabhu, Loki Natarajan, Jingjing Zou

rag nlp benchmarks

arXiv AI

Jul 20

Map as a Prompt: Learning Multi-Modal Spatial-Signal Foundation Models for Cross-scenario Wireless Localization

arXiv:2607. 15713v1 Announce Type: cross Abstract: Accurate and robust wireless localization is a critical enabler for emerging 5G/6G applications, including autonomous driving, extended reality, and smart manufacturing.

By Yong Chu, Xun Zhou, Zenglin Xu, Hui Wang, Yue Yu

agents robotics multimodal benchmarks

arXiv Machine Learning

Jul 21

Hierarchical Wireless Foundation Model for Multi-Task Optimization

arXiv:2607. 16877v1 Announce Type: cross Abstract: The increasing complexity of next-generation wireless networks has driven the integration of artificial intelligence (AI) into wireless communications.

By Yangjing Wang, Ouya Wang, Shenglong Zhou, Geoffrey Ye Li

efficiency

arXiv Machine Learning

Oct 28

AmbientFM: A Foundation Model for Ambient Sensing

AmbientFM is a foundation model designed for ambient sensing using ubiquitous WiFi signals. It is pre‑trained on 9.2 million unlabeled Channel State Information samples collected over 439 days from 20 commercial devices in real homes, making it the largest real‑world WiFi CSI pre‑training corpus reported to date. The model learns transferable wireless representations through contrastive learning, masked reconstruction, and physics‑informed objectives, and with lightweight adaptation it can transfer to nine downstream tasks, achieving above 0.90 AUROC on all classification benchmarks and supporting dense spatial reconstruction.

By Guozhen Zhu, Yuqian Hu, Sakila Jayaweera, Weihang Gao, Wei-Hsiang Wang, Jiaxuan Zhang, Beibei Wang, Chenshu Wu, K. J. Ray Liu

benchmarks safety

arXiv Machine Learning

Aug 6

MoCA: Multi-modal Cross-masked Autoencoder for Digital Health Measurements

arXiv:2506. 02260v4 Announce Type: replace-cross Abstract: Wearable devices enable continuous multi-modal physiological and behavioral monitoring, yet analysis of these data streams faces fundamental challenges including the lack of gold-standard labels and incomplete sensor data.

By Howon Ryu, Yuliang Chen, Yacun Wang, Andrea Z. LaCroix, Chongzhi Di, Loki Natarajan, Yu Wang, Jingjing Zou

llms multimodal benchmarks

arXiv Machine Learning

Aug 7

MoCA: Multi-modal Cross-masked Autoencoder for Time Series in Digital Health

arXiv:2506. 02260v5 Announce Type: replace-cross Abstract: Wearable devices enable continuous multi-modal physiological and behavioral monitoring, yet analysis of these data streams faces fundamental challenges including the lack of gold-standard labels and incomplete sensor data.

By Howon Ryu, Yuliang Chen, Yacun Wang, Andrea Z. LaCroix, Chongzhi Di, Loki Natarajan, Yu Wang, Jingjing Zou

llms multimodal benchmarks

arXiv AI

Jul 14

JEPA for AI-Native 6G: Predictive Representations and Open Challenges

arXiv:2607. 09798v1 Announce Type: cross Abstract: Sixth-generation (6G) networks are moving toward AI-native operation, where learning modules are embedded across the radio access network (RAN), edge, and core.

By Sheikh Salman Hassan, Irshad A. Meer, Almoatssimbillah Saifaldawla, Yan Kyaw Tun, Mustafa Ozger, Madyan Alsenwi, Nguyen Van Huynh, Woong-Hee Lee, Cedomir Stefanovic, Mathini Sellathurai, Henk Wymeersch, Tharmalingam Ratnarajah

rag diffusion benchmarks

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