Semantic segmentation is a challenging task in computer vision. In recent years, the
performance of semantic segmentation has been greatly improved by using deep learning …

Image semantic segmentation is more and more being of interest for computer vision and
machine learning researchers. Many applications on the rise need accurate and efficient …

Depth information has proven to be a useful cue in the semantic segmentation of RGB-D
images for providing a geometric counterpart to the RGB representation. Most existing works …

Abstract Convolutional Neural Networks (CNNs) achieve impressive performance in a wide
variety of fields. Their success benefited from a massive boost when very deep CNN models …

RGB-D semantic segmentation has attracted increasing attention over the past few years.
Existing methods mostly employ homogeneous convolution operators to consume the RGB …

Existing RGB-D salient object detection (SOD) models usually treat RGB and depth as
independent information and design separate networks for feature extraction from each …

We present an approach to semantic scene analysis using deep convolutional networks.
Our approach is based on tangent convolutions-a new construction for convolutional …

Semantic segmentation is a challenging task in computer vision systems. A lot of methods
have been developed to tackle this problem ranging from autonomous vehicles, human …

In this paper, we propose a novel Pattern-Affinitive Propagation (PAP) framework to jointly
predict depth, surface normal and semantic segmentation. The motivation behind it comes …

Recent advances in deep learning have shown excellent performance in various scene
understanding tasks. However, in some complex environments or under challenging …