Self-paced deep clustering: an attention model for unsupervised domain adaptive person re-identification

Reliability-aware clustering and adaptive feature space expansion for unsupervised person re-identification

Beyond Global-Cluster Supervision: Adaptive Feature Fusion and Memory Augmentation for Unsupervised Person Re-Identification

As a critical task in intelligent surveillance and smart city systems, person re-identification (ReID) addresses the challenge of matching individuals across non-overlapping camera views. Although fully unsupervised learning (USL) methods based on pseudo-label training have achieved remarkable progress, they still face...

DTTA: a semantic-anchored network for multi-source person re-identification

Adaptive controlled mutual learning framework for text-image person re-identification

DiffReID: Discriminative Diffusion Model for Object Re-Identification

As a fundamental image processing task, object Re-Identification (ReID) aims to retrieve objects across non-overlapping cameras. Recently, with the development of deep learning, significant advancements have been made in object ReID. However, most existing methods suffer from generalization due to the limited size and...

Clustering-integrated Horizontal Pyramid Feature Embedding for In-domain Unsupervised Person Re-identification

— A cross-domain and unsupervised Re-Identification (Re-ID) is primarily impacted by the challenges presented by domain shift, intra-class heterogeneity across datasets, and the lack of annotated target pictures. When training and testing take place within the same dataset, these influences are lessened in the in-domai...