The goal of semantic segmentation is to segment the input image according to semantic
information and predict the semantic category of each pixel from a given label set. With the …

Abstract RGB-Thermal infrared (RGB-T) image analysis has been actively studied in recent
years. In the past decade, it has received wide attention and made a lot of important …

Semantic segmentation is a fundamental task in computer vision, and it has various
applications in fields such as robotic sensing, video surveillance, and autonomous driving. A …

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 …

Multimodal fusion can make semantic segmentation more robust. However, fusing an
arbitrary number of modalities remains underexplored. To delve into this problem, we create …

Semantic segmentation is important for scene understanding. To address the scenes of
adverse illumination conditions of natural images, thermal infrared (TIR) images are …

Understanding urban scenes is a fundamental ability requirement for assisted driving and
autonomous vehicles. Most of the available urban scene understanding methods use red …

Color–thermal (RGB-T) urban scene parsing has recently attracted widespread interest.
However, most existing approaches to RGB-T urban scene parsing do not deeply explore …

Compared with traditional handcrafted features, deep learning has greatly improved the
performance of scene parsing. However, it remains challenging under various …

Understanding urban scenes is a fundamental capability required of intelligent vehicles.
Depth cues provide useful geometric information for semantic segmentation, thus …