  In keypoint estimation tasks such as human pose estimation, heatmap-based
regression is the dominant approach despite possessing notable drawbacks:
heatmaps intrinsically suffer from quantization error and require excessive
computation to generate and post-process. Motivated to find a more efficient
solution, we propose to model individual keypoints and sets of spatially
related keypoints (i.e., poses) as objects within a dense single-stage
anchor-based detection framework. Hence, we call our method KAPAO (pronounced
"Ka-Pow"), for Keypoints And Poses As Objects. KAPAO is applied to the problem
of single-stage multi-person human pose estimation by simultaneously detecting
human pose and keypoint objects and fusing the detections to exploit the
strengths of both object representations. In experiments, we observe that KAPAO
is faster and more accurate than previous methods, which suffer greatly from
heatmap post-processing. The accuracy-speed trade-off is especially favourable
in the practical setting when not using test-time augmentation. Source code:
https://github.com/wmcnally/kapao.
