  Crowd counting is an application-oriented task and its inference efficiency
is crucial for real-world applications. However, most previous works relied on
heavy backbone networks and required prohibitive run-time consumption, which
would seriously restrict their deployment scopes and cause poor scalability. To
liberate these crowd counting models, we propose a novel Structured Knowledge
Transfer (SKT) framework, which fully exploits the structured knowledge of a
well-trained teacher network to generate a lightweight but still highly
effective student network. Specifically, it is integrated with two
complementary transfer modules, including an Intra-Layer Pattern Transfer which
sequentially distills the knowledge embedded in layer-wise features of the
teacher network to guide feature learning of the student network and an
Inter-Layer Relation Transfer which densely distills the cross-layer
correlation knowledge of the teacher to regularize the student's feature
evolutio Consequently, our student network can derive the layer-wise and
cross-layer knowledge from the teacher network to learn compact yet effective
features. Extensive evaluations on three benchmarks well demonstrate the
effectiveness of our SKT for extensive crowd counting models. In particular,
only using around $6\%$ of the parameters and computation cost of original
models, our distilled VGG-based models obtain at least 6.5$\times$ speed-up on
an Nvidia 1080 GPU and even achieve state-of-the-art performance. Our code and
models are available at {\url{https://github.com/HCPLab-SYSU/SKT}}.
