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New LENS method improves attribute-based person retrieval on unseen cameras

By PulseAugur Editorial · [1 sources] ·

Researchers have developed LENS, a new method for attribute-based person retrieval in surveillance imagery, particularly effective on unseen cameras. LENS combines fine-tuned DINOv3 and CLIP models with a distilled student model. It improves retrieval metrics by regularizing attribute groups, scoring gallery images based on the exact query's log-likelihood, and smoothing gallery logits using DINOv3 appearance features. AI

IMPACT Enhances the accuracy of person retrieval systems in surveillance, particularly in dynamic environments with varying camera conditions.

RANK_REASON The cluster contains a research paper detailing a new method for computer vision tasks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New LENS method improves attribute-based person retrieval on unseen cameras

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The cluster contains a research paper detailing a new method for computer vision tasks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Nguyen Dang Duy Tan, Vo Thanh Nguyen, Linh T. T. Tran, Huynh Nguyen Phat ·

    LENS: Likelihood Evaluation and Neighbor Smoothing for Attribute-Based Person Retrieval on Unseen Cameras

    arXiv:2610.33641v1 Announce Type: new Abstract: Attribute-based person retrieval ranks surveillance images from a binary query of semantic attributes, but mean Average Attribute Degree of Match (mADM) drops on unseen cameras as predicted probabilities shift. Common pipelines rank…

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