Most existing change detection (CD) methods target homogeneous images. However, in
real-world scenarios like disaster management, where CD is urgent and pre-changed and …

Infrared and visible image fusion can generate images that not only highlight prominent
targets, but also contain rich details and texture information. However, directly fusing the …

Trajectory prediction for heterogeneous traffic agents in autonomous driving is a challenging
and crucial task. A large amount of research has laid a solid foundation for this field …

Purpose 3D multimodal medical image deformable registration plays a significant role in
medical image analysis and diagnosis. However, due to the substantial differences between …

The accurate and efficient segmentation of the spine is important in the diagnosis and
treatment of spine malfunctions and fractures. However, it is still challenging because of …

Convolutional neural networks (CNNs) have shown great effectiveness in medical image
segmentation. However, they may be limited in modeling large inter-subject variations in …

Medical image segmentation plays an important role in many image-guided clinical
approaches. However, existing segmentation algorithms mostly rely on the availability of …

Convolutional neural networks are widely used in various segmentation tasks in medical
images. However, they are challenged to learn global features adaptively due to the …

Accurate segmentation of anatomical structures in cardiac magnetic resonance imaging
(MRI) is essential for effective clinical diagnoses and treatment strategies. While deep …

This paper proposes a novel network model named DYOLO, aimed at improving the
accuracy and real-time performance of object detection tasks. This model combines the …