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Related Experiment Video

Updated: Dec 24, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

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Improving Description-based Person Re-identification by Multi-granularity Image-text Alignments.

Kai Niu, Yan Huang, Wanli Ouyang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |April 11, 2020
    PubMed
    Summary
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    This study introduces a novel Multi-granularity Image-text Alignments (MIA) model for description-based person re-identification (Re-id). The MIA model effectively addresses cross-modal fine-grained challenges, significantly improving similarity evaluation in video surveillance.

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Description-based person re-identification (Re-id) is crucial for video surveillance.
    • Challenges include modality heterogeneity (cross-modal problem) and fine-grained matching within categories.
    • Existing methods struggle with direct image-description similarity due to these complexities.

    Purpose of the Study:

    • To propose a novel Multi-granularity Image-text Alignments (MIA) model.
    • To alleviate the cross-modal fine-grained problem in description-based person Re-id.
    • To enhance similarity evaluation between images and textual descriptions.

    Main Methods:

    • Hierarchical alignment across three granularities: global-global, global-local, and local-local.

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    Last Updated: Dec 24, 2025

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    9.5K
    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
    13:44

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

    Published on: August 30, 2013

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  • Global Contrast (GC) module for global context matching.
  • Relation-guided Global-local Alignment (RGA) module for adaptive component highlighting.
  • Bi-directional Fine-grained Matching (BFM) module for part-phrase matching.
  • End-to-end training with a proposed step training strategy.

    • Main Results:

    • Achieved state-of-the-art performance on the CUHK-PEDES dataset.
    • Outperformed previous methods by a significant margin.
    • Demonstrated the effectiveness of multi-granularity alignments for Re-id.

    Conclusions:

    • The MIA model effectively addresses the cross-modal fine-grained problem in person Re-id.
    • Hierarchical alignments at multiple granularities improve similarity evaluation.
    • The proposed method offers a robust and efficient solution for description-based person Re-id tasks.