To alleviate the need for large-scale pixel-wise annotations, domain adaptation for semantic
segmentation trains segmentation models on synthetic data (source) with computer …

With autonomous industries on the rise, domain adaptation of the visual perception stack is
an important research direction due to the cost savings promise. Much prior art was …

Human parsing has attracted considerable research interest due to its broad potential
applications in the computer vision community. In this paper, we explore several useful …

Despite significant progress made in image semantic segmentation, most research has
primarily focused on daytime scenes. Semantic segmentation of nighttime images is equally …

Deep learning based object detection methods have made significant progress in recent
years. However, these methods often suffer from a substantial performance drop when …

Despite their success, unsupervised domain adaptation methods for semantic segmentation
primarily focus on adaptation between image domains and do not utilize other abundant …

Optical flow has made great progress in clean scenes, while suffers degradation under
adverse weather due to the violation of the brightness constancy and gradient continuity …

Despite significant progress in medical image analysis using deep learning, predicting
incidental prostate cancer (iPCa) remains challenging due to subtle differences in …

While significant progress has been achieved in the field of image semantic segmentation,
the majority of research has been primarily concentrated on daytime scenes. Semantic …

Unsupervised domain adaptive (UDA) image segmentation has received more and more
attention in recent years. Domain adaptive methods can align the features of data in different …