  The mainstream crowd counting methods regress density map and integrate it to
obtain counting results. Since the density representation to one head accords
to its adjacent distribution, it embeds the same category objects with variant
values, while human beings counting models the invariant features namely
similarity to objects. Inspired by this, we propose a rational and anthropoid
crowd counting framework. To begin with, we leverage counting scalar as
supervision signal, which provides global and implicit guidance to similar
matters. Then, the large kernel CNN is utilized to imitate the paradigm of
human beings which models invariant knowledge firstly and slides to compare
similarity. Later, re-parameterization on pre-trained paralleled parameters is
presented to cater to the inner-class variance on similarity comparison.
Finally, the Random Scaling patches Yield (RSY) is proposed to facilitate
similarity modeling on long distance dependencies. Extensive experiments on
five challenging benchmarks in crowd counting show the proposed framework
achieves state-of-the-art.
