Data clustering is a commonly used data processing technique in many fields, which divides
objects into different clusters in terms of some similarity measure between data points …

Unsupervised person re-identification (re-ID) aims at learning discriminative representations
for person retrieval from unlabeled data. Recent techniques accomplish this task by using …

Abstract Object Re-identification (Re-ID) aims to identify specific objects across different
times and scenes, which is a widely researched task in computer vision. For a prolonged …

Abstract Domain adaptive object re-ID aims to transfer the learned knowledge from the
labeled source domain to the unlabeled target domain to tackle the open-class re …

Thanks to the recent research development in contrastive learning, the gap of visual
representation learning between supervised and unsupervised approaches has been …

The challenge of unsupervised person re-identification (ReID) lies in learning discriminative
features without true labels. This paper formulates unsupervised person ReID as a multi …

Unsupervised visible-infrared person re-identification is a challenging task due to the large
modality gap and the unavailability of cross-modality correspondences. Cross-modality …

Unsupervised person re-identification (ReID) aims at learning discriminative identity features
without annotations. Recently, self-supervised contrastive learning has gained increasing …

Persons are often occluded by various obstacles in person retrieval scenarios. Previous
person re-identification (re-id) methods, either overlook this issue or resolve it based on an …

Existing alignment-based methods have to employ the pre-trained human parsing models to
achieve the pixel-level alignment, and cannot identify the personal belongings (eg …