Most domain adaptation (DA) methods are based on either a convolutional neural networks
(CNNs) or a vision transformers (ViTs). They align the distribution differences between …

… BA HUNG NGO received the BS degree in control engineering and automation from
Hanoi University of Mining and Geology, Hanoi, Vietnam, in 2014, the MS degree in control …

Abstract Knowledge distillation (KD) and domain adaptation (DA) represent potential research
directions for reducing costs associated with deploying deep neural networks (DNN) in …

Pedestrian attribute recognition (PAR) poses a significant challenge but holds practical
significance in various security applications, including surveillance. In the scope of the UPAR …

The enhanced representational power and broad applicability of deep learning models have
attracted significant interest from the research community in recent years. However, these …

In recent years, many mammographic image analysis methods have been introduced for
improving cancer classification tasks. Two major issues of mammogram classification tasks are …

Semi-supervised domain adaptation (SSDA) is a promising technique for various applications.
It can transfer knowledge learned from a source domain having high-density labeled …

Person attribute recognition and attribute-based person retrieval are two core human-centric
tasks. In the recognition task, the challenge lies in identifying attributes based on a person’s …

In single-source unsupervised domain adaptation (SUDA), it is often assumed that a single-source
domain can cover all target domain features. However, the limitation of labeled …

Due to the unique characteristics of remote sensing (RS) data, it is challenging to collect
richer labeled samples for training the deep learning model compared with the natural image …