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 humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …

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 …

The mainstream crowd counting methods usually utilize the convolution neural network
(CNN) to regress a density map, requiring point-level annotations. However, annotating …

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

Automatic estimation of the number of people in unconstrained crowded scenes is a
challenging task and one major difficulty stems from the huge scale variation of people. In …

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 …

Semi-supervised approaches for crowd counting attract attention, as the fully supervised
paradigm is expensive and laborious due to its request for a large number of images of …

Crowd counting presents enormous challenges in the form of large variation in scales within
images and across the dataset. These issues are further exacerbated in highly congested …

Crowd counting is a fundamental yet challenging task, which desires rich information to
generate pixel-wise crowd density maps. However, most previous methods only used the …