论文标题
有效分组以进行关键点检测
Efficient grouping for keypoint detection
论文作者
论文摘要
深度神经网络在传统关键点检测任务中的成功鼓励研究人员解决新问题并收集更复杂的数据集。 DeepFashion2数据集的大小对KePoint检测任务提出了新的挑战，因为它包括13个服装类别，这些类别涵盖了广泛的关键点（总共294个）。所有关键点的直接预测会导致大量的记忆消耗，缓慢的训练和缓慢的推理时间。本文研究了按键分组方法及其如何影响百年体系结构的性能。我们通过强大的后处理方法提出了一种简单有效的自动分组技术，并将其应用于DeepFashion2时尚地标任务和MS COCO姿势估计任务。这将记忆消耗和推断期间的处理时间分别减少了19％和30％，在训练阶段分别减少了28％和26％，而不会损害准确性。
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.
