Occluded person re-identification (Re-ID) focuses on addressing the occlusion problem
when retrieving the person of interest across non-overlapping cameras. With the increasing …

In person re-identification (Re-ID), the data annotation cost of supervised learning, is huge
and it cannot adapt well to complex situations. Therefore, compared with supervised deep …

Person search is a time-consuming computer vision task that entails locating and
recognizing query people in scenic pictures. Body components are commonly mismatched …

In this work, we propose to address the unsupervised domain adaptive (UDA) person re-id
problem in which the model learns from an unlabeled target domain using a fully annotated …

Point clouds provide a flexible geometric representation for computer vision research.
However, the harsh demands for the number of input points and computer hardware are still …

Most of the loss functions proposed for person reidentification (Re-ID) are expected to be
easy to deploy, efficiently improve network performance, and will not introduce redundant …

Person reidentification (re-ID), which is a significant and potential application in the Internet
of Things (IoT), aims to retrieve pedestrians of interest given a labeled image in a camera …

Person re-identification (re-id) is the task of recognizing an individual across non-
overlapping camera views. Some approaches only rely on extracting global appearance …

Multi-scale design has been considered in recent image super-resolution (SR) works to
explore the hierarchical feature information. Existing multi-scale networks aim at building …

Person re-identification, one of the most challenging tasks in the field of computer vision,
aims to recognize the same person cross different cameras. The local feature information …