The purpose of this study is to examine existing deep learning techniques for addressing
class imbalanced data. Effective classification with imbalanced data is an important area of …

Coral reefs are in decline worldwide and monitoring activities are important for assessing
the impact of disturbance on reefs and tracking subsequent recovery or decline. Monitoring …

In this study, we systematically investigate the impact of class imbalance on classification
performance of convolutional neural networks (CNNs) and compare frequently used …

Bilinear models has been shown to achieve impressive performance on a wide range of
visual tasks, such as semantic segmentation, fine grained recognition and face recognition …

Class imbalance is a common problem in the case of real-world object detection and
classification tasks. Data of some classes are abundant, making them an overrepresented …

Underwater target detection is an important part of ocean exploration, which has important
applications in military and civil fields. Since the underwater environment is complex and …

Ecological remote sensing is being transformed by three-dimensional (3D), multispectral
measurements of forest canopies by unmanned aerial vehicles (UAV) and computer vision …

Marine scientists use remote underwater image and video recording to survey fish species
in their natural habitats. This helps them get a step closer towards understanding and …

In this paper, we present the first large-scale dataset for semantic Segmentation of
Underwater IMagery (SUIM). It contains over 1500 images with pixel annotations for eight …

In recent years, there has been an enormous interest in using deep learning to classify
underwater images to identify various objects, such as fishes, plankton, coral reefs …