  Most of the recent advances in crowd counting have evolved from hand-designed
density estimation networks, where multi-scale features are leveraged to
address the scale variation problem, but at the expense of demanding design
efforts. In this work, we automate the design of counting models with Neural
Architecture Search (NAS) and introduce an end-to-end searched encoder-decoder
architecture, Automatic Multi-Scale Network (AMSNet). Specifically, we utilize
a counting-specific two-level search space. The encoder and decoder in AMSNet
are composed of different cells discovered from micro-level search, while the
multi-path architecture is explored through macro-level search. To solve the
pixel-level isolation issue in MSE loss, AMSNet is optimized with an
auto-searched Scale Pyramid Pooling Loss (SPPLoss) that supervises the
multi-scale structural information. Extensive experiments on four datasets show
AMSNet produces state-of-the-art results that outperform hand-designed models,
fully demonstrating the efficacy of NAS-Count.
