Semantic segmentation usually suffers from a long tail data distribution. Due to the
imbalanced number of samples across categories, the features of those tail classes may get …

Medical image segmentation enables doctors to observe lesion regions better and make
accurate diagnostic decisions. Single-branch models such as U-Net have achieved great …

Loss functions, which govern a deep learning-based optimization process, have been
widely investigated to handle the class imbalanced data issue in crack segmentation …

Existing federated learning paradigms usually extensively exchange distributed models at a
central solver to achieve a more powerful model. However, this would incur severe …

Convolutional neural networks (CNN), especially numerous U-shaped models, have
achieved great progress in retinal vessel segmentation. However, a great quantity of global …

Deep learning-based solutions for semantic segmentation suffer from significant
performance degradation when tested on data with different characteristics than what was …

Abstract Passive Acoustic Monitoring (PAM) has emerged as a pivotal technology for wildlife
monitoring, generating vast amounts of acoustic data. However, the successful application of …

This paper presents the design and development of a coastal fisheries monitoring system
that harnesses artificial intelligence technologies. Application of the system across the …

Abstract Passive Acoustic Monitoring (PAM) has emerged as a pivotal technology for wildlife
monitoring, generating vast amounts of acoustic data. However, the successful application of …

Landsat image segmentation is important for obtaining large-scale land cover maps. The
accuracy of CNN-based Landsat image segmentation highly depends on the quantity and …