Contrastive learning enhanced pseudo-labeling for unsupervised domain adaptation in person re-identification.
Person re-identification (ReID) technology has many applications in intelligent surveillance and public safety. However, the domain difference between the source and target domains makes the generalization ability of the model extremely challenging. To reduce the dependence on labeled data, Unsuperv...
Saved in:
Main Authors: | Xuemei Bai, Yuqing Zhang, Chenjie Zhang, Zhijun Wang |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2025-01-01
|
Series: | PLoS ONE |
Online Access: | https://doi.org/10.1371/journal.pone.0328131 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Dual branch guided contrastive learning for unsupervised pedestrian re-identification
by: REN Hangjia, et al.
Published: (2025-06-01) -
GCN-based unsupervised community detection with refined structure centers and expanded pseudo-labeled set.
by: Bing Guo, et al.
Published: (2025-01-01) -
Reconstructing Domain-Specific Features for Unsupervised Domain-Adaptive Object Detection
by: Shuai Dong, et al.
Published: (2025-05-01) -
Few-Shot Unsupervised Domain Adaptation Based on Refined Bi-Directional Prototypical Contrastive Learning for Cross-Scene Hyperspectral Image Classification
by: Xuebin Tang, et al.
Published: (2025-07-01) -
SHAP-Based Feature Selection for Enhanced Unsupervised Labeling
by: Mary Anne Walauskis, et al.
Published: (2025-01-01)