Autonomous driving, in recent years, has been receiving increasing attention for its potential
to relieve drivers' burdens and improve the safety of driving. In modern autonomous driving …

Convolutional neural network (CNN)-based deep learning (DL) is a powerful, recently
developed image classification approach. With origins in the computer vision and image …

Pretrained general-purpose language models can achieve state-of-the-art accuracies in
various natural language processing domains by adapting to downstream tasks via zero …

Estimating the 3D position and orientation of objects in the environment with a single RGB
camera is a critical and challenging task for low-cost urban autonomous driving and mobile …

Semantic segmentation of urban scenes is an essential component in various applications
of autonomous driving. It makes great progress with the rise of deep learning technologies …

Fully automated vehicles collect information about their road environments to adjust their
driving actions, such as braking and slowing down. The development of artificial intelligence …

Object detection in 3D with stereo cameras is an important problem in computer vision, and
is particularly crucial in low-cost autonomous mobile robots without LiDARs. Nowadays …

One of the main challenges in LiDAR-based 3D object detection is that the sensors often fail
to capture the complete spatial information about the objects due to long distance and …

Lung ultrasound (US) imaging has the potential to be an effective point-of-care test for
detection of COVID-19, due to its ease of operation with minimal personal protection …

The massive and continuous spread of COVID-19 has motivated researchers around the
world to intensely explore, understand, and develop new techniques for diagnosis and …