Medical image analysis helps in resolving clinical issues by examining clinically generated
images. In today's world of deep learning (DL) along with advances in computer vision, the …

Semantic segmentation is a challenging task in computer vision systems. A lot of methods
have been developed to tackle this problem ranging from autonomous vehicles, human …

RGB–thermal scene parsing has recently attracted increasing research interest in the field of
computer vision. However, most existing methods fail to perform good boundary extraction …

Inspired by the recent success of fully convolutional networks (FCN) in semantic
segmentation, we propose a deep smoke segmentation network to infer high quality …

Scene appearance changes drastically throughout the day. Existing semantic segmentation
methods mainly focus on well-lit daytime scenarios and are not well designed to cope with …

Recent research has discovered that deep learning models are vulnerable to membership
inference attacks, which can reveal whether a sample is in the training dataset of the victim …

Although previous works on traffic scene understanding have achieved great success, most
of them stop at a lowlevel perception stage, such as road segmentation and lane detection …

Cross-domain object detection uses knowledge from source domain tasks to enhance the
object detection in target domain. It can reduce the workload of data annotations in the new …

In this paper, we propose a goal-oriented obstacle avoidance navigation system based on
deep reinforcement learning that uses depth information in scenes, as well as goal position …

In an orchard automation process, a current challenge is to recognize natural landmarks and
tree trunks to localize intelligent robots. To overcome low-light conditions and global …