  The performance of human pose estimation depends on the spatial accuracy of
keypoint localization. Most existing methods pursue the spatial accuracy
through learning the high-resolution (HR) representation from input images. By
the experimental analysis, we find that the HR representation leads to a sharp
increase of computational cost, while the accuracy improvement remains marginal
compared with the low-resolution (LR) representation. In this paper, we propose
a design paradigm for cost-effective network with LR representation for
efficient pose estimation, named FasterPose. Whereas the LR design largely
shrinks the model complexity, yet how to effectively train the network with
respect to the spatial accuracy is a concomitant challenge. We study the
training behavior of FasterPose, and formulate a novel regressive cross-entropy
(RCE) loss function for accelerating the convergence and promoting the
accuracy. The RCE loss generalizes the ordinary cross-entropy loss from the
binary supervision to a continuous range, thus the training of pose estimation
network is able to benefit from the sigmoid function. By doing so, the output
heatmap can be inferred from the LR features without loss of spatial accuracy,
while the computational cost and model size has been significantly reduced.
Compared with the previously dominant network of pose estimation, our method
reduces 58% of the FLOPs and simultaneously gains 1.3% improvement of accuracy.
Extensive experiments show that FasterPose yields promising results on the
common benchmarks, i.e., COCO and MPII, consistently validating the
effectiveness and efficiency for practical utilization, especially the
low-latency and low-energy-budget applications in the non-GPU scenarios.
