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 …

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

Robust and accurate geometric understanding against adverse weather conditions is one
top prioritized conditions to achieve a high-level autonomy of self-driving cars. However …

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

The past decade has witnessed the rapid development of autonomous driving systems.
However, it remains a daunting task to achieve full autonomy, especially when it comes to …

Recently, RGB-Thermal based perception has shown significant advances. Thermal
information provides useful clues when visual cameras suffer from poor lighting conditions …

Most existing RGB-D salient object detection (SOD) methods tend to achieve higher
performance by integrating additional modules, such as feature enhancement and edge …