In recent years, person re-identification (ReID) has received much attention since it is a
fundamental task in intelligent surveillance systems and has widespread application …

Human-centric visual tasks have attracted increasing research attention due to their
widespread applications. In this paper, we aim to learn a general human representation from …

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 …

Modern data augmentation using a mixture-based technique can regularize the models from
overfitting to the training data in various computer vision applications, but a proper data …

In this paper, we introduce a large Multi-Attribute and Language Search dataset for text-
based person retrieval, called MALS, and explore the feasibility of performing pre-training on …

Human-centric perceptions (eg, pose estimation, human parsing, pedestrian detection,
person re-identification, etc.) play a key role in industrial applications of visual models. While …

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 …

In humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …

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

Persons are often occluded by various obstacles in person retrieval scenarios. Previous
person re-identification (re-id) methods, either overlook this issue or resolve it based on an …