We present GvSeg, ag eneral v ideo seg mentation framework for addressing four different
video segmentation tasks (ie., instance, semantic, panoptic, and exemplar-guided) while …

Protein representation learning is a challenging task that aims to capture the structure and
function of proteins from their amino acid sequences. Previous methods largely ignored the …

Scene segmentation via unsupervised domain adaptation (UDA) enables the transfer of
knowledge acquired from source synthetic data to real-world target data, which largely …

In recent years, Incomplete Multi-View Clustering (IMVC) has become an important and
challenging task. Although several methods have been proposed to address IMVC, they still …

Abstract Recently, the Segment Anything Model (SAM) has gained substantial attention in
image segmentation due to its remarkable performance. It has demonstrated impressive …

In medical imaging, semantic segmentation is crucial for accurate diagnosis. However, it is
constrained by the scarcity of labeled data. To reduce the dependency on extensive …

Accurate and reliable automatic segmentation of lesion areas in gastroscopy images can
assist endoscopists in making diagnoses and reduce the possibility of missed or incorrect …

Semantic segmentation is of great significance in the medical field, as it can help doctors
intelligently, quickly, and accurately locate key lesion areas, providing crucial support for the …

Activation function, one of the most critical components in deep learning, enables the
artificial neural networks to learn complex patterns through nonlinearity. Currently, element …

Effective transfer learning, within medical image classification, is probably one of the most
critical areas of research due to the associated complexities with the nature of medical …