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 …

Any computer vision application development starts off by acquiring images and data, then
preprocessing and pattern recognition steps to perform a task. When the acquired images …

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

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 …

The key to address clothes-changing person re-identification (re-id) is to extract clothes-
irrelevant features, eg, face, hairstyle, body shape, and gait. Most current works mainly focus …

Abstract Domain adaptive object re-ID aims to transfer the learned knowledge from the
labeled source domain to the unlabeled target domain to tackle the open-class re …

This paper provides a pair similarity optimization viewpoint on deep feature learning, aiming
to maximize the within-class similarity s_p and minimize the between-class similarity s_n …

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 …

Image-to-image translation (I2I) aims to transfer images from a source domain to a target
domain while preserving the content representations. I2I has drawn increasing attention and …