Nighttime Semantic Segmentation

Nighttime semantic segmentation aims to accurately classify and delineate objects in images captured under low-light conditions, a crucial task for applications like autonomous driving. Current research focuses on developing robust models that overcome challenges like poor illumination, reduced contrast, and the lack of annotated nighttime data, employing techniques such as image relighting, cross-modal adaptation (combining image and event camera data), and unsupervised domain adaptation from daytime datasets. These advancements are significantly improving the accuracy and reliability of nighttime scene understanding, paving the way for safer and more efficient autonomous systems and broader computer vision applications.