  Tremendous variation in the scale of people/head size is a critical problem
for crowd counting. To improve the scale invariance of feature representation,
recent works extensively employ Convolutional Neural Networks with multi-column
structures to handle different scales and resolutions. However, due to the
substantial redundant parameters in columns, existing multi-column networks
invariably exhibit almost the same scale features in different columns, which
severely affects counting accuracy and leads to overfitting. In this paper, we
attack this problem by proposing a novel Multi-column Mutual Learning (McML)
strategy. It has two main innovations: 1) A statistical network is incorporated
into the multi-column framework to estimate the mutual information between
columns, which can approximately indicate the scale correlation between
features from different columns. By minimizing the mutual information, each
column is guided to learn features with different image scales. 2) We devise a
mutual learning scheme that can alternately optimize each column while keeping
the other columns fixed on each mini-batch training data. With such
asynchronous parameter update process, each column is inclined to learn
different feature representation from others, which can efficiently reduce the
parameter redundancy and improve generalization ability. More remarkably, McML
can be applied to all existing multi-column networks and is end-to-end
trainable. Extensive experiments on four challenging benchmarks show that McML
can significantly improve the original multi-column networks and outperform the
other state-of-the-art approaches.
