Artificial intelligence (AI) is revolutionizing many aspects of our lives, except it raises
fundamental safety and ethical issues. In this survey paper, we review the current state of …

From pivotal roles in autonomous vehicles, healthcare diagnostics, and surveillance
systems to seamlessly integrating with augmented reality, object detection algorithms stand …

This paper proposes a practical list of safety concerns and mitigation methods for visual
deep learning algorithms. The growing success of deep learning algorithms in solving non …

Object detection (OD) models are seamlessly integrated into numerous intelligent software
systems, playing a crucial role in various tasks. These models are typically constructed upon …

In recent years, the growth of large-scale datasets has significantly propelled the progress of
deep learning applications. Yet, annotating these datasets remains a labor-intensive …

This paper proposes a practical approach to deal with instance-dependent noise in
classification. Supervised learning with noisy labels is one of the major research topics in …

Object detection requires substantial labeling effort for learning robust models. Active
learning can reduce this effort by intelligently selecting relevant examples to be annotated …

Highly performing object detectors require large training datasets, which entail class and
bounding box annotations. To reduce the labelling effort of curating such datasets, Weakly …

The annotation effort associated with object detection is extremely costly. One option to
reduce cost is to relax the demands on annotation quality, effectively allowing annotation …

Deep learning has demonstrated tremendous potential in solving complex computational
tasks such as human re-identification, optical character recognition, and object detection …