We present a novel unsupervised domain adaption method for person re-identification (reID)
that generalizes a model trained on a labeled source domain to an unlabeled target domain …

Although graph neural networks (GNNs) have achieved impressive achievements in graph
classification, they often need abundant task-specific labels, which could be extensively …

Graph neural networks (GNNs) have achieved state-of-the-art results on graph classification
tasks. They have been primarily studied in cases of supervised end-to-end training, which …

Most recent unsupervised person re-identification methods maintain a cluster uni-proxy for
contrastive learning. However, due to the intra-class variance and inter-class similarity, the …

Abstract Re-identification (Re-ID) is a process that seeks to identify concern individuals from
successive non-overlap** photographs. The area of computer vision has recently seen an …

Abstract Unsupervised Person Re-Identification (Re-ID) is challenging due to the lack of
ground-truth labels. Most existing methods address this problem by progressively mining …

Person re-identification (re-ID) is a challenging task that aims to learn discriminative features
for person retrieval. In person re-ID Jaccard distance is a widely used distance metric …

Unsupervised person re-identification (Re-ID) aims to learn semantic representations for
person retrieval without using identity labels. Most existing methods generate fine-grained …

The objective of unsupervised domain adaptation (UDA) for person re-identification (re-ID) is
to associate person in images captured from heterogeneous camera perspectives …

Unsupervised domain adaptation, which transfers knowledge from the source domain to the
target domain, has still been a challenging problem. However, previous domain adaptation …