  In recent years, crowd counting, a technique for predicting the number of
people in an image, becomes a challenging task in computer vision. In this
paper, we propose a cross-column feature fusion network to solve the problem of
information redundancy in columns. We introduce the Information Fusion Module
(IFM) which provides a channel for information flow to help different columns
to obtain significant information from another column. Through this channel,
different columns exchange information with each other and extract useful
features from the other column to enhance key information. Hence, there is no
need for columns to pay attention to all areas in the image. Each column can be
responsible for different regions, thereby reducing the burden of each column.
In experiments, the generalizability of our model is more robust and the
results of transferring between different datasets acheive the comparable
results with the state-of-the-art models.
