While CNN-based models have made remarkable progress on human pose estimation, what spatial dependencies they capture to localize keypoints remains unclear. In this work, we propose a model called \textbf{TransPose}, which introduces Transformer for human pose estimation. The attention layers built in Transformer enable our model to capture long-range relationships efficiently and also can reveal what dependencies the predicted keypoints rely on. To predict keypoint heatmaps, the last attention layer acts as an aggregator, which collects contributions from image clues and forms maximum positions of keypoints. Such a heatmap-based localization approach via Transformer conforms to the principle of Activation Maximization~\cite{erhan2009visualizing}. And the revealed dependencies are image-specific and fine-grained, which also can provide evidence of how the model handles special cases, e.g., occlusion. The experiments show that TransPose achieves 75.8 AP and 75.0 AP on COCO validation and test-dev sets, while being more lightweight and faster than mainstream CNN architectures. The TransPose model also transfers very well on MPII benchmark, achieving superior performance on the test set when fine-tuned with small training costs. Code and pre-trained models are publicly available\footnote{\url{https://github.com/yangsenius/TransPose}}.
翻译:虽然有线电视新闻网基础模型在人造相估测方面取得了显著的进展,但是它们捕捉到的将关键点本地化的空间依赖度仍然不明确。 在这项工作中,我们提议了一个名为\ textbf{TransPose}的模型,它引入了人类造相估测变器。在变异器中构建的注意层使我们的模型能够有效地捕捉长距离关系,并且能够揭示预测关键点所依赖的可靠性。为了预测关键点热图,最后的注意层起到聚合器的作用,它收集图像线索和形成关键点最大位置的贡献。通过变异器这种基于热映射的本地化方法符合激活最大化原则。而暴露出来的依赖度是图象特有和细微的,这也能够证明模型是如何处理特殊案例的,例如,闭路图。实验显示, TransPosePose AP 和 AP CO 校准和 CO 最大关键点位置位置上的 AP 。这种以热映射法为基础的本地化方法,比主流的CNNPC II 测试模型在可使用时, 也能够很好地实现。