Active learning is a paradigm aimed at reducing the annotation effort by training the model
on actively selected informative and/or representative samples. Another paradigm to reduce …

Active Learning has emerged as a viable solution for addressing the challenge of labeling
extensive amounts of data in data-intensive applications such as computer vision and neural …

Artificial Intelligence (AI) has achieved significant advancements in technology and research
with the development over several decades, and is widely used in many areas including …

Active learning aims to reduce the labeling effort that is required to train algorithms by
learning an acquisition function selecting the most relevant data for which a label should be …

Measuring the perceptual quality of images automatically is an essential task in the area of
computer vision, as degradations on image quality can exist in many processes from image …

The upcoming satellite-based Internet of Things (S-IoT) has the capability to provide timely
status updates to massive terrestrial user equipments (UEs) via non-orthogonal multiple …

Abstract The field of Active Domain Adaptation (ADA) has been investigating ways to close
the performance gap between supervised and unsupervised learning settings. Previous …

While learning to rank (LTR) has been widely used in web search to prioritize most relevant
webpages among the retrieved contents subject to the input queries, the traditional LTR …

Weakly supervised object detection which reduces the need for strong supersivison during
training has recently made significant achievements. However, it remains a challenging …

Active learning aims to select samples to be annotated that yield the largest performance
improvement for the learning algorithm. Many methods approach this problem by measuring …