Due to domain shift, a large performance drop is usually observed when a trained crowd
counting model is deployed in the wild. While existing domain-adaptive crowd counting …

RGB-Thermal (RGB-T) crowd counting is a challenging task, which uses thermal images as
complementary information to RGB images to deal with the decreased performance of …

Crowd counting has drawn more and more attention for its significance in reality application.
However, it's still a challenging task because of scale variation in images. In this paper, we …

Crowd counting using deep convolutional neural networks (CNN) has achieved
encouraging progress in recent years. Nevertheless, how to efficiently address the problems …

The accuracy of crowd counting is susceptible to scale variations of crowd head in the
congested scene. Some counting networks, such as crowd density pre-classification …

Huge variations in the scales of people in images create an extremely challenging problem
in the task of crowd counting. Currently, many researchers apply multi-column structures to …

This paper investigates the issue of crowd counting for crowd images. A novel method
named two-task convolutional neural network (T^ 2 2 CNN) is proposed to simultaneously …

The Transformer architecture has been popular in recent crowd counting work since it
breaks the limited receptive field of traditional CNNs. However, since crowd images always …

In this paper, we propose a novel congested crowd counting network for crowd density
estimation, ie, the Adaptive Multi-scale Context Aggregation Network (MSCANet). MSCANet …

Unsupervised cross-domain object counting has recently received great attention in
computer vision, which generalizes the model from the source domain to the unlabeled …