Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …

Semantic-based segmentation (Semseg) methods play an essential part in medical imaging
analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is …

Fast and robust small target detection is one of the key technologies in the infrared (IR)
search and tracking systems. With the development of deep learning, there are many data …

Remote sensing images are useful for a wide variety of planet monitoring applications, from
tracking deforestation to tackling illegal fishing. The Earth is extremely diverse---the amount …

For machine learning systems to be reliable, we must understand their performance in
unseen, out-of-distribution environments. In this paper, we empirically show that out-of …

This paper presents a novel supervised convolutional neural network architecture,“DUCK-
Net”, capable of effectively learning and generalizing from small amounts of medical images …

High spatial resolution (HSR) remote sensing images contain complex foreground-
background relationships, which makes the remote sensing land cover segmentation a …

Downsampling operations such as max pooling or strided convolution are ubiquitously
utilized in Convolutional Neural Networks (CNNs) to aggregate local features, enlarge …

Many biological applications require the segmentation of cell bodies, membranes and nuclei
from microscopy images. Deep learning has enabled great progress on this problem, but …