Image instance segmentation involves labeling pixels of images with classes and instances,
which is one of the pivotal technologies in many domains, such as natural scenes …

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 …

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 …

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 …

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 …

Person re-identification (re-ID) is an important topic in computer vision. This paper studies
the unsupervised setting of re-ID, which does not require any labeled information and thus is …

In this paper, we introduce Random Erasing, a new data augmentation method for training
the convolutional neural network (CNN). In training, Random Erasing randomly selects a …

In the past few years, the field of computer vision has gone through a revolution fueled
mainly by the advent of large datasets and the adoption of deep convolutional neural …

Abstract This paper proposes Attribute Attention Network (AANet), a new architecture that
integrates person attributes and attribute attention maps into a classification framework to …