Person re-identification (Re-ID) aims at retrieving a person of interest across multiple non-
overlapping cameras. With the advancement of deep neural networks and increasing …

Machine learning systems generally assume that the training and testing distributions are
the same. To this end, a key requirement is to develop models that can generalize to unseen …

Person re-identification aims to retrieve persons in highly varying settings across different
cameras and scenarios, in which robust and discriminative representation learning is …

Visible-infrared person re-identification (Re-ID) aims to match the pedestrian images of the
same identity from different modalities. Existing works mainly focus on alleviating the …

Existing fully-supervised person re-identification (ReID) methods usually suffer from poor
generalization capability caused by domain gaps. The key to solving this problem lies in …

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

Talking human face generation aims at synthesizing a natural human face that talks in
correspondence to the given text or audio series. Implementing the recently developed …

Recent advances in person re-identification (ReID) obtain impressive accuracy in the
supervised and unsupervised learning settings. However, most of the existing methods need …

Clothing-change person re-identification (CC Re-ID) aims to match the same person who
changes clothes across cameras. Current methods are usually limited by the insufficient …

Unsupervised domain adaptation (UDA) in the task of person re-identification (re-ID) is
highly challenging due to large domain divergence and no class overlap between domains …