  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 drawbacks of learning a pure one-look
regression model for a counting task. Based on these insights, we enhance
one-look regression counting models by regulating activation maps from the
final convolution layer of the network with coarse ground-truth activation maps
generated from simple dot annotations. We call this strategy heatmap regulation
(HR). We show that this simple enhancement effectively suppresses false
detections generated by the corresponding one-look baseline model and also
improves the performance in terms of false negatives. Evaluations are performed
on four different counting datasets --- two for car counting (CARPK, PUCPR+),
one for crowd counting (WorldExpo) and another for biological cell counting
(VGG-Cells). Adding HR to a simple VGG front-end improves performance on all
these benchmarks compared to a simple one-look baseline model and results in
state-of-the-art performance for car counting.
