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 …

The task of semantic segmentation holds a fundamental position in the field of computer
vision. Assigning a semantic label to each pixel in an image is a challenging task. In recent …

Semantic image segmentation, which becomes one of the key applications in image
processing and computer vision domain, has been used in multiple domains such as …

Recent leading approaches to semantic segmentation rely on deep convolutional networks
trained with human-annotated, pixel-level segmentation masks. Such pixel-accurate …

Deep convolutional neural networks (DCNNs) trained on a large number of images with
strong pixel-level annotations have recently significantly pushed the state-of-art in semantic …

Convolutional Neural Network (CNN) has demonstrated promising performance in single-
label image classification tasks. However, how CNN best copes with multi-label images still …

Recently, significant improvement has been made on semantic object segmentation due to
the development of deep convolutional neural networks (DCNNs). Training such a DCNN …

In this paper, we propose DeepCut, a method to obtain pixelwise object segmentations
given an image dataset labelled weak annotations, in our case bounding boxes. It extends …

Semantic segmentation, also called scene labeling, refers to the process of assigning a
semantic label (eg car, people, and road) to each pixel of an image. It is an essential data …

Semantic image segmentation (SiS) plays a fundamental role in a broad variety of computer
vision applications, providing key information for the global understanding of an image. This …