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 …

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 …

Scene parsing is challenging for unrestricted open vocabulary and diverse scenes. In this
paper, we exploit the capability of global context information by different-region-based …

In this work we address the task of semantic image segmentation with Deep Learning and
make three main contributions that are experimentally shown to have substantial practical …

Deep Convolutional Neural Networks (DCNNs) have recently shown state of the art
performance in high level vision tasks, such as image classification and object detection …

The encoder-decoder framework is state-of-the-art for offline semantic image segmentation.
Since the rise in autonomous systems, real-time computation is increasingly desirable. In …

We introduce a purely feed-forward architecture for semantic segmentation. We map small
image elements (superpixels) to rich feature representations extracted from a sequence of …

In this work we address the task of the contextual classification of an airborne LiDAR point
cloud. For that purpose, we integrate a Random Forest classifier into a Conditional Random …

Human activities often occur in specific scene contexts, eg, playing basketball on a
basketball court. Training a model using existing video datasets thus inevitably captures and …

Context is essential for semantic segmentation. Due to the diverse shapes of objects and
their complex layout in various scene images, the spatial scales and shapes of contexts for …