Object detection using deep learning has attracted considerable interest from researchers
because of its competency in performing state-of-the-art tasks, including detection …

The term" crowd counting" refers to the practise of counting the number of people present in
a certain area. Urban planning, medical services, emergency preparedness, public security …

Rice plant counting is crucial for many applications in rice production, such as yield
estimation, growth diagnosis, disaster loss assessment, etc. Currently, rice counting still …

Perspective distortions and crowd variations make crowd counting a challenging task in
computer vision. To tackle it, many previous works have used multi-scale architecture in …

Recently, crowd density estimation has received increasing attention. The main challenge
for this task is to achieve high-quality manual annotations on a large amount of training data …

Cross-domain crowd counting (CDCC) is a hot topic due to its importance in public safety.
The purpose of CDCC is to alleviate the domain shift between the source and target domain …

Counting objects in remote sensing is crucial for analyzing their distribution in images.
Compared to surveillance perspectives, counting dense objects in remote sensing images is …

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 …

Most previous works on object counting are limited to pre-defined categories. In this paper,
we focus on class-agnostic counting, ie, counting object instances in an image by simply …

Zero-shot object counting (ZOC) aims to enumerate objects in images using only the names
of object classes during testing, without the need for manual annotations. However, a critical …