  The success of deep neural networks in the traditional keypoint detection
task encourages researchers to solve new problems and collect more complex
datasets. The size of the DeepFashion2 dataset poses a new challenge on the
keypoint detection task, as it comprises 13 clothing categories that span a
wide range of keypoints (294 in total). The direct prediction of all keypoints
leads to huge memory consumption, slow training, and a slow inference time.
This paper studies the keypoint grouping approach and how it affects the
performance of the CenterNet architecture. We propose a simple and efficient
automatic grouping technique with a powerful post-processing method and apply
it to the DeepFashion2 fashion landmark task and the MS COCO pose estimation
task. This reduces memory consumption and processing time during inference by
up to 19% and 30% respectively, and during the training stage by 28% and 26%
respectively, without compromising accuracy.
