The goal of semantic segmentation is to segment the input image according to semantic
information and predict the semantic category of each pixel from a given label set. With the …

With the unprecedented developments in deep learning, automatic segmentation of main
abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have …

Full-image dependencies provide useful contextual information to benefit visual
understanding problems. In this work, we propose a Criss-Cross Network (CCNet) for …

Semantic segmentation has a broad range of applications, but its real-world impact has
been significantly limited by the prohibitive annotation costs necessary to enable …

One-shot image semantic segmentation poses a challenging task of recognizing the object
regions from unseen categories with only one annotated example as supervision. In this …

Training a convolutional neural network (CNN) to detect infrared small targets in a fully
supervised manner has gained remarkable research interests in recent years, but is highly …

We consider the problem of unsupervised domain adaptation for semantic segmentation by
easing the domain shift between the source domain (synthetic data) and the target domain …

We propose an embarrassingly simple point annotation scheme to collect weak supervision
for instance segmentation. In addition to bounding boxes, we collect binary labels for a set of …

The success of deep learning in visual recognition tasks has driven advancements in
multiple fields of research. Particularly, increasing attention has been drawn towards its …

This paper presents a weakly supervised instance segmentation method that consumes
training data with tight bounding box annotations. The major difficulty lies in the uncertain …