A semantic understanding of road traffic can help people understand road traffic flow
situations and emergencies more accurately and provide a more accurate basis for anomaly …

Despite the remarkable performance of deep learning methods on various tasks, most
cutting-edge models rely heavily on large-scale annotated training examples, which are …

Localizing individuals in crowds is more in accordance with the practical demands of
subsequent high-level crowd analysis tasks than simply counting. However, existing …

In crowd counting, each training image contains multiple people, where each person is
annotated by a dot. Existing crowd counting methods need to use a Gaussian to smooth …

In crowd counting datasets, each person is annotated by a point, which is usually the center
of the head. And the task is to estimate the total count in a crowd scene. Most of the state-of …

Crowd localization, predicting head positions, is a more practical and high-level task than
simply counting. Existing methods employ pseudo-bounding boxes or pre-designed …

Training a convolutional neural network (CNN) to detect infrared small targets in a fully
supervised manner has gained remarkable research interests in recent years, but is highly …

Scale variation is a deep-rooted problem in object counting, which has not been effectively
addressed by existing scale-aware algorithms. An important factor is that they typically …

The accuracy of crowd counting in images has improved greatly in recent years due to the
development of deep neural networks for predicting crowd density maps. However, most …

Point cloud semantic segmentation often requires largescale annotated training data, but
clearly, point-wise labels are too tedious to prepare. While some recent methods propose to …