Human keypoint detection from a single image is very challenging due to
occlusion, blur, illumination and scale variance of person instances. In this
paper, we find that context information plays an important role in addressing
these issues, and propose a novel method named progressive context refinement
(PCR) for human keypoint detection. First, we devise a simple but effective
context-aware module (CAM) that can efficiently integrate spatial and channel
context information to aid feature learning for locating hard keypoints. Then,
we construct the PCR model by stacking several CAMs sequentially with shortcuts
and employ multi-task learning to progressively refine the context information
and predictions. Besides, to maximize PCR's potential for the aforementioned
hard case inference, we propose a hard-negative person detection mining
strategy together with a joint-training strategy by exploiting the unlabeled
coco dataset and external dataset. Extensive experiments on the COCO keypoint
detection benchmark demonstrate the superiority of PCR over representative
state-of-the-art (SOTA) methods. Our single model achieves comparable
performance with the winner of the 2018 COCO Keypoint Detection Challenge. The
final ensemble model sets a new SOTA on this benchmark.

本文提出了一种称为 PCR 的新方法，通过多任务学习和利用上下文信息来提高人体关键点检测的性能，并通过利用未标记的 coco 数据集和外部数据集来实现难例推理的最大化潜能。实验结果表明，PCR 模型优于代表最先进的模型，并在 COCO 关键点检测基准测试上取得了最新的最佳表现。