[PDF][PDF] Deep unsupervised domain adaptation: A review of recent advances and perspectives

X Liu, C Yoo, F ** from object localization, object pose estimation to grasp estimation for parallel grippers: a review
G Du, K Wang, S Lian, K Zhao - Artificial Intelligence Review, 2021 - Springer
This paper presents a comprehensive survey on vision-based robotic gras**. We
conclude three key tasks during vision-based robotic gras**, which are object localization …

Class-aware sample reweighting optimal transport for multi-source domain adaptation

S Wang, B Wang, Z Zhang, AA Heidari, H Chen - Neurocomputing, 2023 - Elsevier
Abstract Multi-Source Domain Adaptation (MSDA) techniques have attracted widespread
attention due to their availability to transfer knowledge from multiple source domains to the …

[HTML][HTML] Video surveillance using deep transfer learning and deep domain adaptation: Towards better generalization

Y Himeur, S Al-Maadeed, H Kheddar… - … Applications of Artificial …, 2023 - Elsevier
Recently, develo** automated video surveillance systems (VSSs) has become crucial to
ensure the security and safety of the population, especially during events involving large …

Pepnet: Parameter and embedding personalized network for infusing with personalized prior information

J Chang, C Zhang, Y Hui, D Leng, Y Niu… - Proceedings of the 29th …, 2023 - dl.acm.org
With the increase of content pages and interactive buttons in online services such as online-
shop** and video-watching websites, industrial-scale recommender systems face …

[HTML][HTML] Deep learning in automated ultrasonic NDE–developments, axioms and opportunities

S Cantero-Chinchilla, PD Wilcox, AJ Croxford - NDT & E International, 2022 - Elsevier
The analysis of ultrasonic NDE data has traditionally been addressed by a trained operator
manually interpreting data with the support of rudimentary automation tools. Recently, many …