Often the best performing deep neural models are ensembles of multiple base-level
networks, nevertheless, ensemble learning with respect to domain adaptive person re-ID …

This paper focuses on the visible-thermal cross-modality person re-identification (VT Re-ID)
task, whose goal is to match person images between the daytime visible modality and the …

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

Video analysis tasks such as action recognition have received increasing research interest
with growing applications in fields such as smart healthcare, thanks to the introduction of …

Deep learning techniques have achieved impressive progress in the task of person re-
identification. However, how to generalize a learned model from the source domain to the …

Unsupervised domain adaptation (UDA) involves a supervised loss in a labeled source
domain and an unsupervised loss in an unlabeled target domain, which often faces more …

Daytime visible modality (RGB) and night-time infrared (IR) modality person re-identification
(VI-ReID) is a challenging cross-modality pedestrian retrieval problem. However, training a …

Unsupervised domain adaptive (UDA) person re-identification (re-ID) is a challenging task
due to the missing of labels for the target domain data. To handle this problem, some recent …

This study aims to identify types of virtual/augmented reality–based brand experiences
(VR/AR experiences) to understand their impacts on consumer–brand relationships. For this …

Video semantic segmentation is an essential task for the analysis and understanding of
videos. Recent efforts largely focus on supervised video segmentation by learning from fully …