Previous work generally believes that improving the spatial invariance of convolutional
networks is the key to object counting. However, after verifying several mainstream counting …

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 …

Counting high‐density objects quickly and accurately is a popular area of research. Crowd
counting has significant social and economic value and is a major focus in artificial …

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 …

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 …

Existing works on visual counting primarily focus on one specific category at a time, such as
people, animals, and cells. In this paper, we are interested in counting everything, that is to …

Class-agnostic counting (CAC) aims to count all instances in a query image given few
exemplars. A standard pipeline is to extract visual features from exemplars and match them …

In this paper, we focus on the crowd localization task, a crucial topic of crowd analysis. Most
regression-based methods utilize convolution neural networks (CNN) to regress a density …