论文标题
ICCV 2019关键可可和MAPILLARY研讨会关键点检测挑战赛曲目技术报告：分布感知的坐标表示人姿势估计
Joint COCO and Mapillary Workshop at ICCV 2019 Keypoint Detection Challenge Track Technical Report: Distribution-Aware Coordinate Representation for Human Pose Estimation
论文作者
论文摘要
在本文中，我们专注于人类姿势估计中的坐标表示。尽管是标准选择，但尚未系统地研究基于热图的表示。我们发现，坐标解码的过程（即将预测的热图转换为坐标）对于人姿势估计性能而言是显着意义的，尽管如此，以前尚未认识到。鉴于发现的重要性，我们进一步探究了标准坐标解码方法的设计局限性，并提出了一种原则性的分布感知解码方法。同时，我们通过生成准确的热图分布来进行公正的模型训练，改善了标准坐标编码过程（即将地面真实坐标转换为热图）。将它们融合在一起，为关键点（DALK）方法制定了一种新颖的分布感知坐标表示。 Dark用作模型插件，可显着提高各种最先进的人类姿势估计模型的性能。广泛的实验表明，黑暗可以在可可键检测挑战上产生最佳结果，从而验证了我们新颖的坐标表示思想的有用性和有效性。包含更多详细信息的项目页面可在https://ilovepose.github.io/coco
In this paper, we focus on the coordinate representation in human pose estimation. While being the standard choice, heatmap based representation has not been systematically investigated. We found that the process of coordinate decoding (i.e. transforming the predicted heatmaps to the coordinates) is surprisingly significant for human pose estimation performance, which nevertheless was not recognised before. In light of the discovered importance, we further probe the design limitations of the standard coordinate decoding method and propose a principled distribution-aware decoding method. Meanwhile, we improve the standard coordinate encoding process (i.e. transforming ground-truth coordinates to heatmaps) by generating accurate heatmap distributions for unbiased model training. Taking them together, we formulate a novel Distribution-Aware coordinate Representation for Keypoint (DARK) method. Serving as a model-agnostic plug-in, DARK significantly improves the performance of a variety of state-of-the-art human pose estimation models. Extensive experiments show that DARK yields the best results on COCO keypoint detection challenge, validating the usefulness and effectiveness of our novel coordinate representation idea. The project page containing more details is at https://ilovepose.github.io/coco
