This paper aims to count arbitrary objects in images. The leading counting approaches start
from point annotations per object from which they construct density maps. Then, their …

Previous work generally believes that improving the spatial invariance of convolutional
networks is the key to object counting. However, after verifying several mainstream counting …

In this paper, we address the task of object counting in images. We follow modern learning
approaches in which a density map is estimated directly from the input image. We employ …

The counting problem aims to estimate the number of objects in images. Due to large scale
variation and labeling deviations, it remains a challenging task. The static density map …

In this paper, we propose a simple and effective way to improve one-look regression models
for object counting from images. We use class activation map visualizations to illustrate the …

In this paper we explore the scenario of learning to count multiple instances of objects from
images that have been dot-annotated through crowdsourcing. Specifically, we work with a …

Visual counting, a task that predicts the number of objects from an image/video, is an open-
set problem by nature, ie, the number of population can vary in [0,+[?]) in theory. However …

In this paper, we explore the problem of training onelook regression models for counting
objects in datasets comprising a small number of high-resolution, variableshaped images …

In this paper, we introduce the idea of blockwise classification to count objects. The current
mainstream method for counting objects is to regress the density map or to regress the …

Counting objects in digital images is a process that should be replaced by machines. This
tedious task is time consuming and prone to errors due to fatigue of human annotators. The …