To understand the real world using various types of data, Artificial Intelligence (AI) is the
most used technique nowadays. While finding the pattern within the analyzed data …

Microorganisms such as bacteria and fungi play essential roles in many application fields,
like biotechnique, medical technique and industrial domain. Microorganism counting …

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

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 …

This paper investigates crowd counting in the frequency domain, which is a novel direction
compared to the traditional view in the spatial domain. By transforming the density map into …

In this paper, we address the large scale variation problem in crowd counting by taking full
advantage of the multi-scale feature representations in a multi-level network. We implement …

Crowd counting is a fundamental yet challenging task, which desires rich information to
generate pixel-wise crowd density maps. However, most previous methods only used the …

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 …

Semi-supervised approaches for crowd counting attract attention, as the fully supervised
paradigm is expensive and laborious due to its request for a large number of images of …