With the increasing demand for real-world scenarios such as robot navigation and
autonomous driving, how to achieve a good trade-off between segmentation accuracy …

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 this paper, a novel knowledge distillation (KD)-based pedestrian attribute recognition
(PAR) model is developed, where a multi-label mixed feature learning network (MMFL-Net) …

Real-time semantic segmentation is widely used in autonomous driving and robotics. Most
previous networks achieved great accuracy based on a complicated model involving mass …

The tradeoff between speed and accuracy is important in semantic segmentation problems,
especially for resource-constrained platforms, such as intelligent vehicles. In this paper, we …

Extracting mask information of buildings and water areas from high resolution remote
sensing images is beneficial to monitoring and management of urban development …

Extracting buildings and water bodies from high-resolution remote sensing images is of
great significance for urban development planning. However, when studying buildings and …

Optimizing the computational efficiency of the artificial neural networks is crucial for resource-
constrained platforms like autonomous driving systems. To address this challenge, we …

Semantic image segmentation for autonomous driving is a challenging task due to its
requirement for both effectiveness and efficiency. Recent developments in deep learning …

COVID-19 is a fast-growing disease all over the world, but facilities in the hospitals are
restricted. Due to unavailability of an appropriate vaccine or medicine, early identification of …