  In this paper, we propose an efficient human pose estimation network -- SFM
(slender fusion model) by fusing multi-level features and adding lightweight
attention blocks -- HSA (High-Level Spatial Attention). Many existing methods
on efficient network have already taken feature fusion into consideration,
which largely boosts the performance. However, its performance is far inferior
to large network such as ResNet and HRNet due to its limited fusion operation
in the network. Specifically, we expand the number of fusion operation by
building bridges between two pyramid frameworks without adding layers.
Meanwhile, to capture long-range dependency, we propose a lightweight attention
block -- HSA, which computes second-order attention map. In summary, SFM
maximizes the number of feature fusion in a limited number of layers. HSA
learns high precise spatial information by computing the attention of spatial
attention map. With the help of SFM and HSA, our network is able to generate
multi-level feature and extract precise global spatial information with little
computing resource. Thus, our method achieve comparable or even better accuracy
with less parameters and computational cost. Our SFM achieve 89.0 in<EMAIL_ADDRESS>42.0 in<EMAIL_ADDRESS>on MPII validation set and 71.7 in AP, 90.7 in<EMAIL_ADDRESS>on COCO
validation with only 1.7G FLOPs and 1.5M parameters. The source code will be
public soon.
