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 …

Unsupervised domain adaptive person re-identification (UDA re-ID) aims at transferring the
labeled source domain's knowledge to improve the model's discriminability on the unlabeled …

The lack of labeled data is a key challenge for learning useful representation from time
series data. However, an unsupervised representation framework that is capable of …

Prevailing deep convolutional neural networks (CNNs) for person re-IDentification (reID) are
usually built upon ResNet or VGG backbones, which were originally designed for …

Images of road scenes in low-light situations are lack of details which could increase crash
risk of connected autonomous vehicles (CAVs). Therefore, an effective and efficient image …

This paper considers the problem of unsupervised person re-identification (re-ID), which
aims to learn discriminative models with unlabeled data. One popular method is to obtain …

Unsupervised person re-identification (Re-ID) aims to learn discriminative features without
human-annotated labels. Recently, contrastive learning has provided a new prospect for …

One fundamental challenge of vehicle re-identification (re-id) is to learn robust and
discriminative visual representation, given the significant intra-class vehicle variations …

Text-video retrieval is one of the basic tasks for multimodal research and has been widely
harnessed in many real-world systems. Most existing approaches directly compare the …

Re-identification (re-ID) aims to identify a person's images across different cameras.
However, the domain differences between different datasets make it a challenge for re-ID …