Image segmentation is one of the most basic tasks in computer vision and remains an initial
step of many applications. In this paper, we focus on interactive image segmentation (IIS) …

Despite the remarkable performance of deep learning methods on various tasks, most
cutting-edge models rely heavily on large-scale annotated training examples, which are …

Interactive segmentation allows users to extract target masks by making positive/negative
clicks. Although explored by many previous works, there is still a gap between academic …

Deep neural networks have become a mainstream approach to interactive segmentation. As
we show in our experiments, while for some images a trained network provides accurate …

In click-based interactive segmentation, the mask extraction process is dictated by
positive/negative user clicks; however, most existing methods do not fully exploit the user …

High-quality training data play a key role in image segmentation tasks. Usually, pixel-level
annotations are expensive, laborious and time-consuming for the large volume of training …

Deep learning methods, especially convolutional neural networks, have been successfully
applied to lesion segmentation in breast ultrasound (BUS) images. However, pattern …

In this paper, we propose PolyTransform, a novel instance segmentation algorithm that
produces precise, geometry-preserving masks by combining the strengths of prevailing …

In the task of interactive image segmentation, users initially click one point to segment the
main body of the target object and then provide more points on mislabeled regions …

Interactive image segmentation is an essential tool in pixel-level annotation and image
editing. To obtain a high-precision binary segmentation mask, users tend to add interaction …