Object detection is a fundamental visual recognition problem in computer vision and has
been widely studied in the past decades. Visual object detection aims to find objects of …

Physics-Informed Neural Networks (PINNs) have emerged recently as a promising
application of deep neural networks to the numerical solution of nonlinear partial differential …

The crowded scenario not only contains instances at various scales but also introduces a
variety of occlusion patterns ranging from non-occluded situations to heavily occluded …

Abstract Physics-Informed Neural Networks (PINNs) have emerged recently as a promising
application of deep neural networks to the numerical solution of nonlinear partial differential …

Single-stage instance segmentation approaches have recently gained popularity due to
their speed and simplicity, but are still lagging behind in accuracy, compared to two-stage …

Understanding interactions between humans and objects is one of the fundamental
problems in visual classification and an essential step towards detailed scene …

Feature warping is a core technique in optical flow estimation; however, the ambiguity
caused by occluded areas during warping is a major problem that remains unsolved. In this …

The backbone of most deep-learning-based continuous sign language recognition (CSLR)
models consists of a visual module, a sequential module, and an alignment module …

Pedestrian detection is an important branch of computer vision, and has important
applications in the fields of autonomous driving, artificial intelligence and video surveillance …

Although significant progress has been made in pedestrian detection recently, pedestrian
detection in crowded scenes is still challenging. The heavy occlusion between pedestrians …