Existing multi-person pose estimators can be roughly divided into two-stage approaches (top-down and bottom-up approaches) and one-stage approaches. The two-stage methods either suffer high computational redundancy for additional person detectors or group keypoints heuristically after predicting all the instance-free keypoints. The recently proposed single-stage methods do not rely on the above two extra stages but have lower performance than the latest bottom-up approaches. In this work, a novel single-stage multi-person pose regression, termed SMPR, is presented. It follows the paradigm of dense prediction and predicts instance-aware keypoints from every location. Besides feature aggregation, we propose better strategies to define positive pose hypotheses for training which all play an important role in dense pose estimation. The network also learns the scores of estimated poses. The pose scoring strategy further improves the pose estimation performance by prioritizing superior poses during non-maximum suppression (NMS). We show that our method not only outperforms existing single-stage methods and but also be competitive with the latest bottom-up methods, with 70.2 AP and 77.5 AP75 on the COCO test-dev pose benchmark. Code is available at https://github.com/cmdi-dlut/SMPR.
翻译:现有多人配置估计值可大致分为两个阶段(自上而下和自下而上的方法)和一个阶段的方法。两阶段方法要么在预测所有无实例关键点之后,对额外个人探测器或组关键点进行高计算冗余,要么在预测所有无实例关键点之后,对额外个人探测器或组关键点进行超常反应。最近提议的单阶段方法并不依赖以上两个额外阶段,但业绩低于最新的自下而上方法。在这项工作中,提出了一个新的单一阶段多人构成倒退,称为SMPR。它遵循了每个地点密集预测和预测实例觉关键点的模式。除了特征汇总外,我们提出了更好的战略,为培训确定积极假设,这些假设在密集的构成估计中都发挥重要作用。网络还学习了估计配置的分数。 组合评分战略通过在非最大抑制期间优先考虑高压力,进一步提高了组合的性能。我们显示,我们的方法不仅优于现有的单阶段方法,而且与最新的底调方法具有竞争力,AP和77.5 APGUP/AB C 基准在70.2 AS-MASM/AM ASV ASV ASV ASV ASV