  Density regression has been widely employed in crowd counting. However, the
frequency imbalance of pixel values in the density map is still an obstacle to
improve the performance. In this paper, we propose a novel learning strategy
for learning error-driven curriculum, which uses an additional network to
supervise the training of the main network. A tutoring network called TutorNet
is proposed to repetitively indicate the critical errors of the main network.
TutorNet generates pixel-level weights to formulate the curriculum for the main
network during training, so that the main network will assign a higher weight
to those hard examples than easy examples. Furthermore, we scale the density
map by a factor to enlarge the distance among inter-examples, which is well
known to improve the performance. Extensive experiments on two challenging
benchmark datasets show that our method has achieved state-of-the-art
performance.
