Learning disentangled representation implicitly via transformer for occluded person re-identification

Learning disentangled representation implicitly via transformer for occluded person re-identification

Person re-IDentification (re-ID) under various occlusions has been a long-standing challenge as person images with different types of occlusions often suffer from misalignment in image matching and ranking. Most existing methods tackle this challenge by aligning spatial features of body parts accord...

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Bibliographic Details
Main Authors: Jia, Mengxi, Cheng, Xinhua, Lu, Shijian, Zhang, Jian
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2022
Subjects:
Engineering::Computer science and engineering
Person Re-Identification
Representation Learning
Online Access:https://hdl.handle.net/10356/162960
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Institution: Nanyang Technological University
Language: English

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https://hdl.handle.net/10356/162960

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