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 …

Attention mechanism has demonstrated great potential in fine-grained visual recognition
tasks. In this paper, we present a counterfactual attention learning method to learn more …

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

Cross-modality person re-identification is a challenging task due to large cross-modality
discrepancy and intra-modality variations. Currently, most existing methods focus on …

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

Inspired by the human cognitive system, attention is a mechanism that imitates the human
cognitive awareness about specific information, amplifying critical details to focus more on …

Unsupervised domain adaptive person re-identification (UDA re-ID) aims at transferring the
labeled source domain's knowledge to improve the model's discriminability on the unlabeled …

In this paper, we present a large scale unlabeled person re-identification (Re-ID) dataset"
LUPerson" and make the first attempt of performing unsupervised pre-training for improving …

Abstract Domain generalizable (DG) person re-identification (ReID) is a challenging
problem because we cannot access any unseen target domain data during training. Almost …