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.
翻译:从单一图像中探测人类关键点非常困难,因为封闭、模糊、照明和规模的人际情况存在差异。在本文件中,我们发现,背景信息在解决这些问题方面发挥着重要作用,并提出一种名为渐进背景完善的人类关键点探测新颖方法。首先,我们设计了一个简单而有效的环境认知模块(CAM),能够有效地整合空间和引导背景信息,帮助查找硬关键点的特征学习。然后,我们建造PCR模型,将几个CAM相继堆积在捷径上,并采用多任务学习来逐步完善背景信息和预测。此外,为了最大限度地发挥PCR在上述硬案例推论中的潜力,我们提出一个硬负人探测采矿战略和联合培训战略,利用无标签的coco数据集和外部数据集。关于CO关键点检测基准的广泛实验表明PCR优于具有代表性的状态(SOTA)方法。我们的单一模型取得了与2018年CO关键点检测基准的赢家之间的可比业绩。