Street-level scene segmentation aims to label each pixel of street-view images into specific
semantic categories. It has been attracting growing interest due to various real-world …

Semantic segmentation of street view images is an important step in scene understanding
for autonomous vehicle systems. Recent works have made significant progress in pixel-level …

This paper exploits the intrinsic features of urban-scene images and proposes a general add-
on module, called height-driven attention networks (HANet), for improving semantic …

In this paper, we address the scene segmentation task by capturing rich contextual
dependencies based on the self-attention mechanism. Unlike previous works that capture …

Semantic segmentation is a key step in scene understanding for autonomous driving.
Although deep learning has significantly improved the segmentation accuracy, current high …

Over the past few years, deep convolutional neural network-based methods have made
great progress in semantic segmentation of street scenes. Some recent methods align …

Street Scene Parsing (SSP) is a fundamental and important step for autonomous driving and
traffic scene understanding. Recently, Fully Convolutional Network (FCN) based methods …

In this article, we propose a Dual Relation-aware Attention Network (DRANet) to handle the
task of scene segmentation. How to efficiently exploit context is essential for pixel-level …

Domain-generalized urban-scene semantic segmentation (USSS) aims to learn generalized
semantic predictions across diverse urban-scene styles. Unlike generic domain gap …

Recent advancements in deep learning have shown an exciting promise in the urban street
scene segmentation. However, many objects, such as poles and sign symbols, are relatively …