[2210.05513] ViFiCon: Vision and Wireless Association Via Self-Supervised Contrastive Learning Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2210.05513 (cs) [Submitted on 11 Oct 2022 ( v1 ), last revised 24 Apr 2026 (this version, v2)] Title: ViFiCon: Vision and Wireless Association Via Self-Supervised Contrastive Learning Authors: Nicholas Meegan , Hansi Liu , Bryan Bo Cao , Abrar Alali , Kristin Dana , Marco Gruteser , Shubham Jain , Ashwin Ashok View a PDF of the paper titled ViFiCon: Vision and Wireless Association Via Self-Supervised Contrastive Learning, by Nicholas Meegan and 6 other authors View PDF HTML (experimental) Abstract: We introduce ViFiCon, a self-supervised contrastive scheme which learns a cross-modal association between vision and wireless modalities. Specifically, the system uses pedestrian data collected from RGB-D camera footage and WiFi Fine Time Measurements (FTM) from a user's smartphone device. Depth data from RGB-D (vision domain) is inherently linked with an observable pedestrian, but FTM data (wireless domain) is associated only to a smartphone on the network. We represent temporal sequences from both vision and wireless domains by stacking multi-person depth data sequences within an image representation. This simplicity allows both scene-wide processing and fewer vision and wireless features, alleviating privacy and energy associated with transmitting IMU data. To facilitate self-supervised learning, we design a scene-wide synchronization pretext task for our network and then employ the learned representation for the downstream multimodal association task. We show that compared to fully supervised state-of-the-art models, ViFiCon achieves high performance vision-to-wireless association of 92.63% in 25 frames sliding window fashion (2.5s), finding which bounding box corresponds to which smartphone device, without hand-labeled association examples for training data. Extensive experimental results demonstrate ViFiCon applicability in real-world systems when wireless data annotations are scarce. Comments: 8 pages, 6 figures, 6 tables Subjects: Computer Vision and Pattern Recognition (cs.CV) MSC classes: 65D19, 68T45 ACM classes: I.4.0; I.4.10; I.5.4 Cite as: arXiv:2210.05513 [cs.CV] (or arXiv:2210.05513v2 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2210.05513 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Bryan Bo Cao [ view email ] [v1] Tue, 11 Oct 2022 15:04:05 UTC (1,732 KB) [v2] Fri, 24 Apr 2026 00:31:49 UTC (3,528 KB) Full-text links: Access Paper: View a PDF of the paper titled ViFiCon: Vision and Wireless Association Via Self-Supervised Contrastive Learning, by Nicholas Meegan and 6 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2022-10 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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