Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

In recent years, urgent needs for counting crowds and vehicles have greatly promoted
research of crowd counting and density estimation. Benefiting from the rapid development of …

Localizing individuals in crowds is more in accordance with the practical demands of
subsequent high-level crowd analysis tasks than simply counting. However, existing …

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 crowd counting, each training image contains multiple people, where each person is
annotated by a dot. Existing crowd counting methods need to use a Gaussian to smooth …

Abstract Convolutional Neural Network (CNN) based methods generally take crowd
counting as a regression task by outputting crowd densities. They learn the mapping …

Scale variation is a deep-rooted problem in object counting, which has not been effectively
addressed by existing scale-aware algorithms. An important factor is that they typically …

Class-agnostic counting (CAC) aims to count all instances in a query image given few
exemplars. A standard pipeline is to extract visual features from exemplars and match them …

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 …

Accurately estimating the number of objects in a single image is a challenging yet
meaningful task and has been applied in many applications such as urban planning and …