Deep supervised learning algorithms typically require a large volume of labeled data to
achieve satisfactory performance. However, the process of collecting and labeling such data …

Abstract Person Re-Identification (Re-ID) is a critical aspect of visual surveillance systems,
which aims to automatically recognize and locate individuals across a multi-camera network …

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

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 …

Thanks to the recent research development in contrastive learning, the gap of visual
representation learning between supervised and unsupervised approaches has been …

We present a novel unsupervised domain adaption method for person re-identification (reID)
that generalizes a model trained on a labeled source domain to an unlabeled target domain …

Unsupervised person re-identification (Re-ID) aims at finding the most informative features
from unlabeled person datasets. Some recent approaches adopted camera-aware …

In person re-identification (ReID) task, it is still challenging to learn discriminative
representation by deep learning, due to limited data. Generally speaking, the model will get …

Unsupervised person re-identification (Re-Id) has attracted increasing attention due to its
practical application in the read-world video surveillance system. The traditional …

Videos stored on mobile devices or delivered on the Internet are usually in compressed
format and are of various unknown compression parameters, but most video super …