论文标题
暗示
HintPose
论文作者
论文摘要
大多数自上而下的姿势估计模型都假定只有一个人在边界框中。但是，假设并不总是正确的。在这份技术报告中，我们向现有的姿势估计器介绍了两个想法：实例提示和经常性改进器，以便该模型能够正确处理多个人的检测框。当我们在COCO17关键点数据集上评估我们的模型时，与基线模型相比，它显示出不可忽略的改进。我们的模型以单个模型的形式获得了76.2映射，而77.3 MAP作为测试-DEV集合的集合，而无需其他训练数据。在使用单独的改进网络进行了其他后处理后，我们的最终预测在可可测试-DEV集上获得了77.8 MAP。
Most of the top-down pose estimation models assume that there exists only one person in a bounding box. However, the assumption is not always correct. In this technical report, we introduce two ideas, instance cue and recurrent refinement, to an existing pose estimator so that the model is able to handle detection boxes with multiple persons properly. When we evaluated our model on the COCO17 keypoints dataset, it showed non-negligible improvement compared to its baseline model. Our model achieved 76.2 mAP as a single model and 77.3 mAP as an ensemble on the test-dev set without additional training data. After additional post-processing with a separate refinement network, our final predictions achieved 77.8 mAP on the COCO test-dev set.
