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 is a challenging task due to many challenges such as scale variations and
noisy background. To handle these challenges, we propose a novel framework named Multi …

This paper focuses on the unsupervised domain adaptation problem for video-based crowd
counting, in which we use labeled data as source domain and unlabelled video data as …

Intelligent transportation system (ITS) collects numerous data for analysis of the
transportation system. The data can be used for providing services for travellers and traffic …

Severe scale variation has become a challenging issue for hindering the improvement of
accuracy in crowd counting task. To tackle the problem, we propose a Pyramid Transformer …

In recent years, researchers have developed many deep-learning-based methods to count
crowd numbers in static images. However, much fewer works focus on video-based crowd …

Existing crowd counting designs usually exploit multi-branch structures to address the scale
diversity problem. However, branches in these structures work in a competitive rather than …

Object counting, which aims to count the accurate number of object instances in images, has
been attracting more and more attention. However, challenges such as large-scale variation …

In recent years, significant progress has been made on the research of crowd counting.
However, as the challenging scale variations and complex scenes existed in crowds, neither …

The class-agnostic counting (CAC) problem has garnered significant attention recently due
to its broad societal applications and formidable challenges. Existing approaches to …