Recent advances in deep learning have shown excellent performance in various scene
understanding tasks. However, in some complex environments or under challenging …

Image segmentation is the task of associating pixels in an image with their respective object
class labels. It has a wide range of applications in many industries including healthcare …

In recent years, considerable progress has been made in semantic segmentation of images
with favorable environments. However, the environmental perception of autonomous driving …

Semantic segmentation of nighttime images plays an equally important role as that of
daytime images in autonomous driving, but the former is much more challenging due to poor …

The performance of nighttime semantic segmentation is restricted by the poor illumination
and a lack of pixel-wise annotation, which severely limit its application in autonomous …

Semantic segmentation has made striking progress due to the success of deep
convolutional neural networks. Considering the demands of autonomous driving, real-time …

Due to the scarcity of dense pixel-level semantic annotations for images recorded in adverse
visual conditions, there has been a keen interest in unsupervised domain adaptation (UDA) …

We explore the zero-shot setting for day-night domain adaptation. The traditional domain
adaptation setting is to train on one domain and adapt to the target domain by exploiting …

The semantic segmentation of nighttime scenes is a challenging problem that is key to
impactful applications like self-driving cars. Yet, it has received little attention compared to its …

Semantic segmentation on driving-scene images is vital for autonomous driving. Although
encouraging performance has been achieved on daytime images, the performance on …