Abstract Unsupervised Domain Adaptive (UDA) person re-identification (ReID) aims at
adapting the model trained on a labeled source-domain dataset to a target-domain dataset …

Person re-IDentification (re-ID) under various occlusions has been a long-standing
challenge as person images with different types of occlusions often suffer from misalignment …

The necessity for a Person ReID system for rapidly evolving urban surveillance applications
is severely challenged by domain shifts-variations in data distribution that occur across …

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

Many unsupervised domain adaptive (UDA) person ReID approaches combine clustering-
based pseudo-label prediction with feature fine-tuning. However, because of domain gap …

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

Gait recognition has emerged as a promising technique for the long-range retrieval of
pedestrians, providing numerous advantages such as accurate identification in challenging …

Unsupervised domain adaptive classifcation intends to improve the classifcation
performance on unlabeled target domain. To alleviate the adverse effect of domain shift …

Deep domain adaptation has achieved promising results in cross-domain hyperspectral
image (HSI) classification. However, existing methods often focus on aligning data …

Clustering-based unsupervised domain adaptive (UDA) person re-identification (ReID)
reduces exhaustive annotations. However, owing to unsatisfactory feature embedding and …