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Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...
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Local attraction refers to disturbances in compass readings caused by magnetic influences from nearby objects such as metal fences, buried pipes, vehicles, buildings, power lines, or natural iron ore deposits. Small items like wristwatches, steel tools, or belt buckles can also interfere with the compass by creating local magnetic fields that distort the Earth's natural magnetic field. These distortions lead to inaccurate readings, posing navigation and land surveying challenges.Local...
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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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Cross-Modality Person Re-Identification via Local Paired Graph Attention Network.

Jianglin Zhou1, Qing Dong1, Zhong Zhang1

  • 1Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin 300387, China.

Sensors (Basel, Switzerland)
|April 28, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new Local Paired Graph Attention Network (LPGAT) for cross-modality person re-identification (ReID). The model effectively bridges the gap between RGB and infrared (IR) pedestrian images by considering paired local features and using contrastive learning.

Keywords:
cross-modalitygraph attention networkperson ReID

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Cross-modality person re-identification (ReID) addresses the challenge of matching pedestrian images between different visual spectra, such as RGB and infrared (IR).
  • Existing graph-based methods for cross-modality ReID often overlook the inherent correlations within paired IR and RGB images, potentially limiting performance.
  • Bridging the modality gap between RGB and IR imagery remains a significant challenge in computer vision.

Purpose of the Study:

  • To propose a novel graph model, the Local Paired Graph Attention Network (LPGAT), for enhancing cross-modality person re-identification.
  • To address the limitations of existing methods by incorporating paired local features and contextual information.
  • To improve the accuracy of person re-identification across different modalities by learning a more robust distance metric.

Main Methods:

  • Development of the Local Paired Graph Attention Network (LPGAT) model, utilizing paired local features from different modalities as graph nodes.
  • Introduction of a contextual attention coefficient that incorporates distance information to refine information propagation within the graph.
  • Implementation of Cross-Center Contrastive Learning (C3L) to optimize the learning of the distance metric by constraining feature distributions relative to heterogeneous centers.

Main Results:

  • Experimental validation on the RegDB and SYSU-MM01 datasets demonstrates the effectiveness of the proposed LPGAT approach.
  • The method successfully leverages paired local features and attention mechanisms to improve cross-modality matching accuracy.
  • Cross-Center Contrastive Learning contributes to learning a more discriminative feature representation for ReID.

Conclusions:

  • The proposed LPGAT model, incorporating paired local features and contextual attention, offers a significant advancement in cross-modality person re-identification.
  • The integration of Cross-Center Contrastive Learning further enhances the model's ability to learn discriminative features across modalities.
  • The approach provides a feasible and effective solution for matching pedestrians between RGB and IR imagery.