Storing Less, Finding More: How Novelty Filtering Improves Cross-Modal Retrieval on Edge Cameras
arXiv:2603.29631v1 Announce Type: cross Abstract: Always-on edge cameras generate continuous video streams where redundant frames degrade cross-modal retrieval by crowding correct results out of top-k search. This paper presents a streaming retrieval architecture: an on-device epsilon-net filter retains only semantically novel frames, building a denoised embedding index; a cross-modal adapter and cloud re-ranker compensate for the compact encoder's weak alignment. A single-pass streaming filter outperforms offline alternatives (k-means, farthest-point, uniform, random) across eight vision-language models (8M-632M) on two egocentric datasets (AEA, EPIC-KITCHENS). Combined, the architecture reaches 45.6% Hit@5 on held-out data using an 8M on-device encoder at an estimated 2.7 mW.
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Abstract:Always-on edge cameras generate continuous video streams where redundant frames degrade cross-modal retrieval by crowding correct results out of top-k search. This paper presents a streaming retrieval architecture: an on-device epsilon-net filter retains only semantically novel frames, building a denoised embedding index; a cross-modal adapter and cloud re-ranker compensate for the compact encoder's weak alignment. A single-pass streaming filter outperforms offline alternatives (k-means, farthest-point, uniform, random) across eight vision-language models (8M-632M) on two egocentric datasets (AEA, EPIC-KITCHENS). Combined, the architecture reaches 45.6% Hit@5 on held-out data using an 8M on-device encoder at an estimated 2.7 mW.
Comments: 6 pages, 3 figures, 5 tables; supplementary video included as ancillary file
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Distributed, Parallel, and Cluster Computing (cs.DC); Information Retrieval (cs.IR)
ACM classes: I.4.9; I.2.10
Cite as: arXiv:2603.29631 [cs.CV]
(or arXiv:2603.29631v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2603.29631
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Sherif Abdelwahab [view email] [v1] Tue, 31 Mar 2026 11:54:38 UTC (8,495 KB)
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