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 …

Person re-identification (Re-ID) technology plays an increasingly crucial role in intelligent
surveillance systems. Widespread occlusion significantly impacts the performance of person …

Most salient object detection approaches use U-Net or feature pyramid networks (FPN) as
their basic structures. These methods ignore two key problems when the encoder …

The challenge of unsupervised person re-identification (ReID) lies in learning discriminative
features without true labels. This paper formulates unsupervised person ReID as a multi …

As an essential problem in computer vision, salient object detection (SOD) has attracted an
increasing amount of research attention over the years. Recent advances in SOD are …

RGB-D salient object detection (SOD) is usually formulated as a problem of classification or
regression over two modalities, ie, RGB and depth. Hence, effective RGB-D feature …

The main purpose of RGB-D salient object detection (SOD) is how to better integrate and
utilize cross-modal fusion information. In this paper, we explore these issues from a new …

Recent deep learning based salient object detection methods achieve gratifying
performance built upon Fully Convolutional Neural Networks (FCNs). However, most of them …

Existing person re-identification (re-id) methods either assume the availability of well-
aligned person bounding box images as model input or rely on constrained attention …

Existing RGB-D salient object detection (SOD) approaches concentrate on the cross-modal
fusion between the RGB stream and the depth stream. They do not deeply explore the effect …