With the rapid advancement of deep learning, the performance of crowd counting has
improved significantly. Nonetheless, existing crowd counting models primarily depend on a …

This work considers supervised learning to count from images and their corresponding point
annotations. Where density-based counting methods typically use the point annotations only …

Generalized counting has recently emerged to count novel-class objects within a query
image, leveraging limited exemplars. Although methods based on exemplar-query pairs …

Object detection models are commonly used for people counting (and localization) in many
applications but require a dataset with costly bounding box annotations for training. Given …

Automated crowd counting has emerged as a vision-based measurement method for crowd
analysis and management. However, current methods based on density maps still suffer …

Few-shot object counting has garnered significant attention for its practicality as it aims to
count target objects in a query image based on given exemplars without the need for …

Aedes aegypti is still one of the main concerns when it comes to disease vectors. Among the
many ways to deal with it, there are important protocols that make use of egg numbers in …

Recent work in crowd counting focuses on counting over detected individuals rather than
estimating the number of people in the image. However, existing crowd localization methods …

Recent advancements in crowd counting methods have relied on density maps as an
intermediary representation for counting, whereby the ground truth of the density map is …

Dense crowd counting remains a challenging task due to complex backgrounds, large-scale
scenes, diversity of human features, and computational performance requirements. Unlike …