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Found 4 papers, 2 papers with code
SiCL: Silhouette-Driven Contrastive Learning for Unsupervised Person Re-Identification with Clothes Change
In this paper, we address a highly challenging yet critical task: unsupervised long-term person re-identification with clothes change.
Ranked #1 on
Unsupervised Person Re-Identification
on PRCC
Clothes Changing Person Re-Identification
Contrastive Learning
+3
Unsupervised Long-Term Person Re-Identification with Clothes Change
We investigate unsupervised person re-identification (Re-ID) with clothes change, a new challenging problem with more practical usability and scalability to real-world deployment.
Clustering
Unsupervised Long Term Person Re-Identification
+2
Hybrid Contrastive Learning with Cluster Ensemble for Unsupervised Person Re-identification
The most popular approaches to tackle unsupervised person ReID are usually performing a clustering algorithm to yield pseudo labels at first and then exploit the pseudo labels to train a deep neural network.
Ranked #1 on
Unsupervised Person Re-Identification
on DukeMTMCreID
(MAP metric)
Cluster-guided Asymmetric Contrastive Learning for Unsupervised Person Re-Identification
To be specific, we propose a novel cluster-level contrastive loss to help the siamese network effectively mine the invariance in feature learning with respect to the cluster structure within and between different data augmentation views, respectively.
