This work reviews the results of the NTIRE 2021 Challenge on Non-Homogeneous
Dehazing. The proposed techniques and their results have been evaluated on a novel …

Extracting robust feature representation is one of the key challenges in object re-
identification (ReID). Although convolution neural network (CNN)-based methods have …

For the visible-infrared person re-identification (VIReID) task, one of the major challenges is
the modality gaps between visible (VIS) and infrared (IR) images. However, the training …

Person re-identification aims to retrieve persons in highly varying settings across different
cameras and scenarios, in which robust and discriminative representation learning is …

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 …

Most works on person re-identification (ReID) take advantage of large backbone networks
such as ResNet, which are designed for image classification instead of ReID, for feature …

Although supervised person re-identification (Re-ID) methods have shown impressive
performance, they suffer from a poor generalization capability on unseen domains …

Recently, with the advance of deep Convolutional Neural Networks (CNNs), person Re-
Identification (Re-ID) has witnessed great success in various applications. However, with …

In recent years, with the increasing demand for public safety and the rapid development of
intelligent surveillance networks, person re-identification (Re-ID) has become one of the hot …

Transformers are more and more popular in computer vision, which treat an image as a
sequence of patches and learn robust global features from the sequence. However, pure …