Machine learning systems generally assume that the training and testing distributions are
the same. To this end, a key requirement is to develop models that can generalize to unseen …

Data-driven machine learning (ML) is promoted as one potential technology to be used in
next-generation wireless systems. This led to a large body of research work that applies ML …

Adaptive person re-identification (adaptive ReID) targets at transferring learned knowledge
from the labeled source domain to the unlabeled target domain. Pseudo-label-based …

This paper aims to learn a domain-generalizable (DG) person re-identification (ReID)
representation from large-scale videos without any annotation. Prior DG ReID methods …

Convolutional neural networks have been widely applied to medical image segmentation
and have achieved considerable performance. However, the performance may be …

Source-free object detection (SFOD) aims to transfer a detector pre-trained on a label-rich
source domain to an unlabeled target domain without seeing source data. While most …

Domain generalizable person re-identification (DG ReID) is a challenging problem, because
the trained model is often not generalizable to unseen target domains with different …

Person re-identification is a challenging cross-camera matching problem, which is inherently
subject to domain shift. To mitigate it, many solutions have been proposed so far, based on …

A crucial aspect of reliable machine learning is to design a deployable system for
generalizing new related but unobserved environments. Domain generalization aims to …

In object detection, data amount and cost are a trade-off, and collecting a large amount of
data in a specific domain is labor-intensive. Therefore, existing large-scale datasets are …