Availability of very high-resolution remote sensing images and advancement of deep
learning methods have shifted the paradigm of image classification from pixel-based and …

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 …

Detection Transformer (DETR) and Deformable DETR have been proposed to eliminate the
need for many hand-designed components in object detection while demonstrating good …

Semantic segmentation requires both rich spatial information and sizeable receptive field.
However, modern approaches usually compromise spatial resolution to achieve real-time …

Semantic segmentation is a challenging task that addresses most of the perception needs of
intelligent vehicles (IVs) in an unified way. Deep neural networks excel at this task, as they …

Abstract The Mapillary Vistas Dataset is a novel, large-scale street-level image dataset
containing 25,000 high-resolution images annotated into 66 object categories with …

Abstract Convolution Neural Network (CNN) has boosted the per-
formanceofalotofcomputervisiontasks, likeimageclassi-fication [31], segmentation [25], and …

Recent advances in deep learning, especially deep convolutional neural networks (CNNs),
have led to significant improvement over previous semantic segmentation systems. Here we …

Recent works have made great progress in semantic segmentation by exploiting richer
context, most of which are designed from a spatial perspective. In contrast to previous works …

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 …