  Recently, the problem of inaccurate learning targets in crowd counting draws
increasing attention. Inspired by a few pioneering work, we solve this problem
by trying to predict the indices of pre-defined interval bins of counts instead
of the count values themselves. However, an inappropriate interval setting
might make the count error contributions from different intervals extremely
imbalanced, leading to inferior counting performance. Therefore, we propose a
novel count interval partition criterion called Uniform Error Partition (UEP),
which always keeps the expected counting error contributions equal for all
intervals to minimize the prediction risk. Then to mitigate the inevitably
introduced discretization errors in the count quantization process, we propose
another criterion called Mean Count Proxies (MCP). The MCP criterion selects
the best count proxy for each interval to represent its count value during
inference, making the overall expected discretization error of an image nearly
negligible. As far as we are aware, this work is the first to delve into such a
classification task and ends up with a promising solution for count interval
partition. Following the above two theoretically demonstrated criterions, we
propose a simple yet effective model termed Uniform Error Partition Network
(UEPNet), which achieves state-of-the-art performance on several challenging
datasets. The codes will be available at:
https://github.com/TencentYoutuResearch/CrowdCounting-UEPNet.
