This research presents the idea of activity fusion into existing Pose Estimation architectures to enhance their predictive ability. This is motivated by the rise in higher level concepts found in modern machine learning architectures, and the belief that activity context is a useful piece of information for the problem of pose estimation. To analyse this concept we take an existing deep learning architecture and augment it with an additional 1x1 convolution to fuse activity information into the model. We perform evaluation and comparison on a common pose estimation dataset, and show a performance improvement over our baseline model, especially in uncommon poses and on typically difficult joints. Additionally, we perform an ablative analysis to indicate that the performance improvement does in fact draw from the activity information.

翻译：这项研究提出了将活动融入现有波斯估计结构以增强其预测能力的设想,其动机是现代机器学习结构中较高层次概念的上升,以及认为活动背景是影响估计问题的有用信息。为了分析这一概念,我们采用现有的深层学习结构,并增加1x1的变化,将活动信息纳入模型。我们对共同的构成估计数据集进行评估和比较,并显示与我们的基线模型相比的绩效改进,特别是在异常的外观和典型的困难连接方面。此外,我们进行了一种模拟分析,以表明绩效改进实际上借鉴了活动信息。