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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

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Published on: January 18, 2020

Memory-augmented shuffled meta learning for visible-infrared person re-identification.

Hanxiao Wu¹, Yutao Chen², Yi Xie³

¹College of Information Science and Engineering, Huaqiao University, Xiamen, 361021, Fujian, China; School of Computer Science and Artificial intelligence, Wuhan University of Technology, Wuhan, 430070, Hubei, China.

Neural Networks : the Official Journal of the International Neural Network Society

|July 17, 2025

Summary

This study introduces memory-augmented shuffled meta (MASM) learning to improve visible-infrared person re-identification (VIPR). MASM enhances feature learning for better cross-modality individual recognition, outperforming existing methods.

Keywords:

Memory-augmentation Meta learning Video surveillance system Visible–infrared person re-identification

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Visible-infrared person re-identification (VIPR) faces challenges due to modality differences, leading to poor cross-modality similarity.
Existing VIPR methods struggle with limited data, hindering discriminative feature learning and global similarity metric capture.

Purpose of the Study:

To develop a novel approach, memory-augmented shuffled meta (MASM) learning, to overcome VIPR challenges.
To enhance the model's ability to learn discriminative features and global similarity metrics for improved cross-modality re-identification.

Main Methods:

Introduced memory-augmented shuffled meta (MASM) learning, combining shuffled meta learning (SML) and memory meta learning (MML).
SML generates diverse training sets, while MML utilizes memory banks for long-term dependencies.
The approach aims to improve data utilization and learn comprehensive global meta metrics.

Main Results:

The MASM method demonstrated superior performance in extensive experiments on the RegDB and SYSU-MM01 datasets.
The approach significantly improved the ability to distinguish individuals across visible and infrared modalities.

Conclusions:

MASM learning effectively addresses the challenges of visible-infrared person re-identification.
The proposed method offers a promising advancement for cross-modality person recognition systems.