论文标题
重新考虑自下而上的人姿势估计的热图回归
Rethinking the Heatmap Regression for Bottom-up Human Pose Estimation
论文作者
论文摘要
当今人类姿势估计方法的热图回归已成为最普遍的选择。通常通过2D高斯内核覆盖所有骨骼关键点来构建地面真实热图。这些内核的标准偏差是固定的。但是，对于需要处理大量人类量表并标记歧义的自下而上的方法，当前的做法似乎是不合理的。为了更好地应对这些问题，我们提出了自适应热图回归（SAHR）方法，该方法可以适应每个关键点的标准偏差。这样，SAHR对各种人类量表和标记歧义更具宽容性。但是，SAHR可能会加剧前背景样本之间的不平衡，这可能会损害SAHR的改善。因此，我们进一步引入了体重自适应热图回归（WAHR），以帮助平衡前后的样品。广泛的实验表明，与WAHR一起，SAHR很大程度上提高了自下而上的人姿势估计的准确性。结果，我们最终以 +1.5AP的最新模型优于最新模型，并在可可Test-Dev2017上实现72.0AP，这与大多数自上而下方法的性能相当可观。源代码可从https://github.com/greatlog/swahr-humanpose获得。
Heatmap regression has become the most prevalent choice for nowadays human pose estimation methods. The ground-truth heatmaps are usually constructed via covering all skeletal keypoints by 2D gaussian kernels. The standard deviations of these kernels are fixed. However, for bottom-up methods, which need to handle a large variance of human scales and labeling ambiguities, the current practice seems unreasonable. To better cope with these problems, we propose the scale-adaptive heatmap regression (SAHR) method, which can adaptively adjust the standard deviation for each keypoint. In this way, SAHR is more tolerant of various human scales and labeling ambiguities. However, SAHR may aggravate the imbalance between fore-background samples, which potentially hurts the improvement of SAHR. Thus, we further introduce the weight-adaptive heatmap regression (WAHR) to help balance the fore-background samples. Extensive experiments show that SAHR together with WAHR largely improves the accuracy of bottom-up human pose estimation. As a result, we finally outperform the state-of-the-art model by +1.5AP and achieve 72.0AP on COCO test-dev2017, which is com-arable with the performances of most top-down methods. Source codes are available at https://github.com/greatlog/SWAHR-HumanPose.
