Abstract Machine learning has made significant advances in the field of image processing.
The foundation of this success is supervised learning, which necessitates annotated labels …

Advances in both parallel processing capabilities because of graphical processing units
(GPUs) and computer vision algorithms have led to the development of deep neural …

Plant leaf diseases cause a decrease in crop yield and degrade the quality, which presents
the urgent need for leaf disease identification. Recently, deep learning technologies …

We propose a metadata-aware self-supervised learning (SSL) framework useful for fine-
grained classification and ecological mapping of bird species around the world. Our …

Unmanned aerial vehicle (UAV) platforms are increasingly used to obtain plant phenotypes
in crop breeding for their efficiency and versatility. A lightweight UAV was used to collect …

Fine-grained ocean ship classification plays a crucial role in maritime military surveillance,
traffic management, and antismuggling operations. However, the complex backgrounds of …

In computer vision, fine-grained classification has become an important issue in recognizing
objects with slight visual differences. Usually, it is challenging to generate good performance …

From traditional machine learning to the latest deep learning classifiers, most models
require a large amount of labeled data to perform optimal training and obtain the best …

Brain MRI-based hippocampus segmentation is considered as an important biomedical
method for prevention, early detection, and accurate diagnosis of neurodegenerative …

Over the decades in the sphere of deep learning and machine learning, supervised learning
has stood to be the anchor but the enormous amount of unannotated data, high cost in the …