当核方法满足特征学习时：用于Skeleton数据动作识别的对数协方差网络
When Kernel Methods meet Feature Learning: Log-Covariance Network for Action Recognition from Skeletal Data
摘要：
由于最近引入的3D传感器，从骨骼数据中识别人体动作是一个热门的研究课题，并且在计算机视觉的许多开放领域应用中是重要的。在文献中，朴素的方法简单地将现成的技术从视频转移到骨架表示。然而，目前的最先进的是竞争之间的不同范例：基于内核的方法和特征学习（循环）神经网络。这两种方法都表现出很强的性能，但它们都表现出严重的，但互补的缺点。出于这一事实的动机，我们的工作旨在结合在一起的两个范例中最好的，通过提出一种方法，其中一个浅网络与协方差表示。我们的直觉是，只要动态被有效地建模，分类网络就不需要很深，也不需要经常性地得分。我们在6个公开数据集的广泛实验分析中验证了这一假设。
英文摘要：
Human action recognition from skeletal data is a hot research topic and important in many open domain applications of computer vision, thanks to recently introduced 3D sensors. In the literature, naive methods simply transfer off-the-shelf techniques from video to the skeletal representation. However, the current state-of-the-art is contended between to different paradigms: kernel-based methods and feature learning with (recurrent) neural networks. Both approaches show strong performances, yet they exhibit heavy, but complementary, drawbacks. Motivated by this fact, our work aims at combining together the best of the two paradigms, by proposing an approach where a shallow network is fed with a covariance representation. Our intuition is that, as long as the dynamics is effectively modeled, there is no need for the classification network to be deep nor recurrent in order to score favorably. We validate this hypothesis in a broad experimental analysis over 6 publicly available datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
