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 …

Top-down methods for monocular human mesh recovery have two stages:(1) detect human
bounding boxes;(2) treat each bounding box as an independent single-human mesh …

Recently, indiscernible scene understanding has attracted a lot of attention in the vision
community. We further advance the frontier of this field by systematically studying a new …

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 …

We tackle the task of Class Agnostic Counting, which aims to count objects in a novel object
category at test time without any access to labeled training data for that category. All …

In this paper, we propose a new agency-guided semi-supervised counting approach. First,
we build a learnable auxiliary structure, namely the density agency to bring the recognized …

Crowd counting targets for determining the number of pedestrians in an image, which is of
crucial importance for smart city construction. The problem of scale variation is an ingrained …

In recent decades, remote sensing object counting has attracted increasing attention from
academia and industry due to its potential benefits in urban traffic, public safety, and road …

Crowd counting has achieved significant progress by training regressors to predict instance
positions. In heavily crowded scenarios however regressors are challenged by …

An emerging Internet of Video Things (IoVT) application, crowd counting is a computer
vision task where the number of heads in a crowded scene is estimated. In recent years, it …