Refining Uncertain Features With Self-Distillation for Face Recognition and Person Re-Identification-Reference-Cited by-同舟云学术

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Refining Uncertain Features With Self-Distillation for Face Recognition and Person Re-Identification

Published:2024 Issue: Volume:26 Page:6981-6995
ISSN:1520-9210
Container-title:IEEE Transactions on Multimedia
language:
Short-container-title:IEEE Trans. Multimedia

Author:

Ou Fu-Zhao¹^ORCID,Chen Xingyu²^ORCID,Zhao Kai³^ORCID,Wang Shiqi¹^ORCID,Wang Yuan-Gen⁴^ORCID,Kwong Sam⁵^ORCID

Affiliation:

1. Department of Computer Science, City University of Hong Kong, Hong Kong SAR, China

2. the TikTok Ecommerce, Shanghai, China

3. Department of Radiological Sciences, University of California, Los Angeles, CA, USA

4. School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China

5. Department of Computing and Decision Sciences, Lingnan University, Hong Kong SAR, China

Funder

Hong Kong Innovation and Technology Commission

Research Grant Council of Hong Kong General Research Fund

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6046/10384483/10415557.pdf?arnumber=10415557

Reference117 articles.

1. Robust Face Recognition via Multimodal Deep Face Representation

2. Disentangled Spectrum Variations Networks for NIR–VIS Face Recognition

3. Deep Learning for Person Re-identification: A Survey and Outlook

4. VehicleNet: Learning Robust Visual Representation for Vehicle Re-Identification

5. Improving Face Recognition from Hard Samples via Distribution Distillation Loss

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1. Robust face recognition model based sample mining and loss functions;Knowledge-Based Systems;2024-10

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