Generalization to out-of-distribution (OOD) data is a capability natural to humans yet
challenging for machines to reproduce. This is because most learning algorithms strongly …

Traditional machine learning paradigms are based on the assumption that both training and
test data follow the same statistical pattern, which is mathematically referred to as …

Deep neural networks (DNNs) have revolutionized artificial intelligence but often lack
performance when faced with out-of-distribution data, a common scenario due to the …

Abstract Domain generalization person re-identification (DG ReID) aims to train a model on
source domains and generalize well on unseen domains. Vision Transformer usually yields …

Clothing-change person re-identification (CC Re-ID) aims to match the same person who
changes clothes across cameras. Current methods are usually limited by the insufficient …

This paper proposes a novel test-time adaptation strategy that adjusts the model pre-trained
on the source domain using only unlabeled online data from the target domain to alleviate …

Abstract Domain Generalizable (DG) person ReID is a challenging task which trains a model
on source domains yet generalizes well on target domains. Existing methods use source …

With various face presentation attacks arising under unseen scenarios, face anti-spoofing
(FAS) based on domain generalization (DG) has drawn growing attention due to its …

Abstract Domain Generalizable Person Re-Identification (DG-ReID) is a more practical ReID
task that is trained from multiple source domains and tested on the unseen target domains …

Recent studies show that, both explicit deep feature matching as well as large-scale and
diverse training data can significantly improve the generalization of person re-identification …