  In this work, we explore the cross-scale similarity in crowd counting
scenario, in which the regions of different scales often exhibit high visual
similarity. This feature is universal both within an image and across different
images, indicating the importance of scale invariance of a crowd counting
model. Motivated by this, in this paper we propose simple but effective
variants of pooling module, i.e., multi-kernel pooling and stacked pooling, to
boost the scale invariance of convolutional neural networks (CNNs), benefiting
much the crowd density estimation and counting. Specifically, the multi-kernel
pooling comprises of pooling kernels with multiple receptive fields to capture
the responses at multi-scale local ranges. The stacked pooling is an equivalent
form of multi-kernel pooling, while, it reduces considerable computing cost.
Our proposed pooling modules do not introduce extra parameters into model and
can easily take place of the vanilla pooling layer in implementation. In
empirical study on two benchmark crowd counting datasets, the stacked pooling
beats the vanilla pooling layer in most cases.
