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 …

Extracting robust feature representation is one of the key challenges in object re-
identification (ReID). Although convolution neural network (CNN)-based methods have …

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 …

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 …

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 …

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 …

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 …

For person re-identification (re-id), attention mechanisms have become attractive as they
aim at strengthening discriminative features and suppressing irrelevant ones, which …