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Uniformity Attentive Learning-Based Siamese Network for Person Re-Identification.

Dasol Jeong¹, Hasil Park¹, Joongchol Shin¹

¹Department of Image, Graduate School of Advanced Imaging Science, Multimedia and Film, Chung-Ang University, Seoul 06974, Korea.

Sensors (Basel, Switzerland)

|July 2, 2020

Summary

This study introduces a new Siamese network for person re-identification (Re-ID) that effectively handles misalignment and occlusion by focusing on both common and distinctive features using a novel attention module and loss function.

Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Keywords:

Siamese network attention mechanism person re-identification

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Person re-identification (Re-ID) faces challenges like misalignment and occlusion, hindering robust feature learning.

Current attention-based Re-ID methods often overlook distinctive features, focusing only on common ones.

Robust feature learning is crucial for addressing intra-class variations in person Re-ID.