As a special type of transformer, vision transformers (ViTs) can be used for various computer
vision (CV) applications. Convolutional neural networks (CNNs) have several potential …

Image segmentation for video analysis plays an essential role in different research fields
such as smart city, healthcare, computer vision and geoscience, and remote sensing …

The mainstream crowd counting methods usually utilize the convolution neural network
(CNN) to regress a density map, requiring point-level annotations. However, annotating …

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 …

Supervised crowd counting relies heavily on costly manual labeling, which is difficult and
expensive, especially in dense scenes. To alleviate the problem, we propose a novel …

The most significant technical challenges of current aerial image object-detection tasks are
the extremely low accuracy for detecting small objects that are densely distributed within a …

Crowd counting research has made significant advancements in real-world applications, but
it remains a formidable challenge in cross modal settings. Most existing methods rely solely …

Object counting aims to estimate the number of objects in images. The leading counting
approaches focus on single-category counting tasks and achieve impressive performance …

Crowd counting in aerial images presents unique challenges due to varying altitudes,
angles, and cluttered backgrounds. Additionally, the small size of targets, often occupying …

Crowd analysis from drones has attracted increasing attention in recent times due to the
ease of use and affordable cost of these devices. However, how this technology can provide …