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Hugging Face Trending Papers

Beyond Gait: Person Identification from Millimeter-Wave Point Clouds Across Activities of Daily Living

The paper explores person identification using millimeter‑wave point clouds beyond traditional gait analysis, focusing on seven activities of daily living (ADLs). It introduces the mm‑ADL dataset of 11 subjects and proposes an activity‑conditioned framework that routes each clip to an activity‑specific identity expert via a supervised mixture of experts. Experiments show that hard routing improves closed‑set ID accuracy from 62.1% to 68.0% and significantly boosts re‑identification metrics, demonstrating the benefit of activity context under controlled indoor conditions.

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arXiv Machine Learning
Oct 19

From User Recognition to Activity Counting: An Identity-Agnostic Approach to Multi-User WiFi Sensing

The paper proposes an identity‑agnostic approach to multi‑user WiFi sensing that reframes activity recognition as counting how many users perform each activity, rather than assigning actions to specific individuals. Using WiMANS data, the identity‑agnostic counting model outperforms identity‑dependent predictions on integer‑count mean absolute error across all tested environments, and remains robust when users are unseen. Embedding analysis shows that identity‑agnostic representations cluster by activity composition instead of user identity, supporting the claim that counting is a more accurate and robust scene‑level estimation method.

By Kemal Bayik, Olayinka Ajayi, Daniel Roggen, Philip Birch
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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
benchmarkssafety
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arXiv Computer Vision
Sep 11

MMGait: Benchmarking and Unifying Gait Recognition across Heterogeneous Modalities

MMGait is a large‑scale multi‑sensor benchmark that aligns visible, infrared, depth, LiDAR, and radar observations at the sequence level, enabling evaluation of single‑modal, cross‑modal, and multi‑modal gait recognition. The study shows that modality rankings shift with probe conditions, cross‑modal alignment remains challenging, and fusion can yield complementary gains. To address the scalability issue of training separate experts, the authors propose Omni‑Modal Gait Recognition and its implementation, OmniGait++, which unifies all recognition settings within a shared identity space using modality‑specific front ends, a shared encoder, and an anchor‑guided fusion module. whyItMatters":"MMGait provides a common testbed for heterogeneous gait sensing and demonstrates that unified recognition across varying modality availability is feasible, offering a scalable alternative to task‑specific experts."

By Saihui Hou, Chenye Wang, Qingyuan Cai, Aoqi Li, Yongzhen Huang
multimodalbenchmarkssafety
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