Abstract Person Re-Identification (Re-ID) is a critical aspect of visual surveillance systems,
which aims to automatically recognize and locate individuals across a multi-camera network …

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

Occluded person re-identification (Re-ID) is a challenging task as persons are frequently
occluded by various obstacles or other persons, especially in the crowd scenario. To …

Due to the modality gap between visible and infrared images with high visual ambiguity,
learning diverse modality-shared semantic concepts for visible-infrared person re …

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 …

Abstract Knowledge distillation is a widely used paradigm for inheriting information from a
complicated teacher network to a compact student network and maintaining the strong …

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

Unsupervised learning visible-infrared person re-identification (USL-VI-ReID) is an
extremely important and challenging task, which can alleviate the issue of expensive cross …

Occluded person re-identification is a challenging task as human body parts could be
occluded by some obstacles (eg trees, cars, and pedestrians) in certain scenes. Some …