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 …

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

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 …

Scene understanding based on image segmentation is a crucial component of autonomous
vehicles. Pixel-wise semantic segmentation of RGB images can be advanced by exploiting …

Deep multimodal fusion by using multiple sources of data for classification or regression has
exhibited a clear advantage over the unimodal counterpart on various applications. Yet …

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 …

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 …

Compared to RGB semantic segmentation, RGBD semantic segmentation can achieve
better performance by taking depth information into consideration. However, it is still …

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

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 …