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 …

Generative adversarial networks (GANs) present a way to learn deep representations
without extensively annotated training data. These networks achieve learning through …

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 …

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 …

We address the problem of visible-infrared person re-identification (VI-reID), that is,
retrieving a set of person images, captured by visible or infrared cameras, in a cross-modal …

Occluded person re-identification (ReID) aims at matching occluded person images to
holistic ones across different camera views. Target Pedestrians (TP) are often disturbed by …

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 …

Occluded person re-identification (ReID) aims to match occluded person images to holistic
ones across dis-joint cameras. In this paper, we propose a novel framework by learning high …

As an instance-level recognition problem, person re-identification (ReID) relies on
discriminative features, which not only capture different spatial scales but also encapsulate …

Person re-identification (re-id) remains challenging due to significant intra-class variations
across different cameras. Recently, there has been a growing interest in using generative …