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Related Experiment Video

Updated: May 29, 2026

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

Harnessing Knowledge From Pretrained VLMs for Unsupervised Person Search.

Yanling Tian, Shanshan Zhang, Di Chen

    IEEE Transactions on Neural Networks and Learning Systems
    |May 27, 2026
    PubMed
    Summary
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    This study introduces FMUPS, a new unsupervised person search method using semantic information from vision-language models (VLMs) to create reliable pseudo-labels. It overcomes challenges in generating accurate bounding boxes and identities for better pedestrian detection and re-identification.

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Person search combines pedestrian detection and re-identification, crucial for surveillance and robotics.
    • Labeling data for supervised training is costly and time-consuming.
    • Unsupervised person search is desirable but challenged by noisy pseudo-labels from low-quality detections.

    Purpose of the Study:

    • To develop a novel unsupervised person search method (FMUPS) that leverages semantic information for reliable pseudo-label generation.
    • To address the challenges of inaccurate bounding boxes and misclassifications in unsupervised learning for person search.
    • To improve the performance of person re-identification by mitigating noise in pseudo-labels.

    Main Methods:

    • Utilizing vision-language models (VLMs) for semantic representations to guide pseudo-label extraction and reduce background noise.

    Related Experiment Videos

    Last Updated: May 29, 2026

    From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
    12:08

    From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

    Published on: August 13, 2014

  • Introducing an anti-bounding-box-noise re-identification loss to correct localization and classification errors.
  • Developing a CLIP ID labeler that uses text-image alignment for pseudo-ID generation and refinement.

    • Main Results:

    • FMUPS effectively generates reliable pseudo-labels by leveraging semantic information from VLMs.
    • The anti-bounding-box-noise re-ID loss successfully alleviates localization and classification noise, enhancing re-ID feature learning.
    • Experimental results on CUHK-SYSU and PRW benchmarks demonstrate superior performance compared to previous unsupervised and weakly supervised methods.

    Conclusions:

    • The proposed FMUPS method significantly improves unsupervised person search by effectively utilizing semantic information.
    • Leveraging VLMs and specialized loss functions offers a promising direction for robust person search in real-world scenarios.
    • The method demonstrates the potential for accurate person search without the need for extensive manual labeling.