STEERER: Resolving Scale Variations for Counting and Localization via
  Selective Inheritance Learning
Tao Han Lei Bai Lingbo Liu Wanli Ouyang
Scale variation is a deep-rooted problem in object counting, which has not
been effectively addressed by existing scale-aware algorithms. An important
factor is that they typically involve cooperative learning across
multi-resolutions, which could be suboptimal for learning the most
discriminative features from each scale. In this paper, we propose a novel
method termed STEERER (\textbf{S}elec\textbf{T}iv\textbf{E}
inh\textbf{ER}itance l\textbf{E}a\textbf{R}ning) that addresses the issue of
scale variations in object counting. STEERER selects the most suitable scale
for patch objects to boost feature extraction and only inherits discriminative
features from lower to higher resolution progressively. The main insights of
STEERER are a dedicated Feature Selection and Inheritance Adaptor (FSIA), which
selectively forwards scale-customized features at each scale, and a Masked
Selection and Inheritance Loss (MSIL) that helps to achieve high-quality
density maps across all scales. Our experimental results on nine datasets with
counting and localization tasks demonstrate the unprecedented scale
generalization ability of STEERER. Code is available at
\url{https://github.com/taohan10200/STEERER}.