Humans can naturally and effectively find salient regions in complex scenes. Motivated by
this observation, attention mechanisms were introduced into computer vision with the aim of …

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 …

Recent advances in deep neural networks (DNNs) have mainly focused on innovations in
network architecture and loss function. In this paper, we introduce a flexible high-order …

Visible-infrared person re-identification (VI-ReID) is a challenging cross-modality pedestrian
retrieval problem. Due to the large intra-class variations and cross-modality discrepancy with …

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 …

In the task of person re-identification, the attention mechanism and fine-grained information
have been proved to be effective. However, it has been observed that models often focus on …

Cross-modality person re-identification (cm-ReID) is a challenging but key technology for
intelligent video analysis. Existing works mainly focus on learning modality-shared …

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 …

We address the pixel-wise mapping problem that commonly exists in the fine-grained
computer vision tasks, such as estimating keypoint heatmaps and segmentation masks …

Many previous methods on text-based person retrieval tasks are devoted to learning a latent
common space mapping, with the purpose of extracting modality-invariant features from both …