Bi‐level deep mutual learning assisted multi‐task network for occluded person re‐identification
Author:
Affiliation:
1. Jiangsu Normal University Kewen College Xuzhou China
2. School of Information and Control Engineering China University of Mining and Technology Xuzhou China
3. Xuzhou First People's Hospital Xuzhou China
Funder
National Natural Science Foundation of China
Natural Science Foundation of Jiangsu Province
Publisher
Institution of Engineering and Technology (IET)
Subject
Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1049/ipr2.12688
Reference56 articles.
1. HPILN: a feature learning framework for cross‐modality person re‐identification
2. Training approach using the shallow model and hard triplet mining for person re‐identification
3. Appearance model update based on online learning and soft‐biometrics traits for people re‐identification in multi‐camera environments
4. Occlusion-sensitive Person Re-identification via Attribute-based Shift Attention
5. Semantic-Aware Occlusion-Robust Network for Occluded Person Re-Identification
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