Unsupervised Domain Adaptation Via Dynamic Clustering and Co-Segment Attentive Learning for Video-Based Person Re-Identification

Unsupervised Domain Adaptation Via Dynamic Clustering and Co-Segment Attentive Learning for Video-Based Person Re-Identification

Currently, supervised person re-identification (Re-ID) models trained on labeled datasets can achieve high recognition performance in the same data domain. However, accuracy drops dramatically when these models are directly applied to other unlabeled datasets or natural environments, due to a signif...

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Bibliographic Details
Main Authors: Fuping Zhang, Fengjun Chen, Zhonggen Su, Jianming Wei
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Person re-identification
unsupervised domain adaptation
dynamic clustering
co-segment attentive learning
Online Access:https://ieeexplore.ieee.org/document/10433542/

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