【15-31期VALSE Webinar活动】 报告嘉宾：马述高（波士顿大学）
报告时间：2015年10月8日（星期四）晚20:45（北京时间）
报告题目：Space-Time Tree Ensemble for Action Recognition [Slides]
主持人：蓝振忠（卡耐基梅隆大学）
报告摘要：Human actions are, inherently, structured patterns of body movements. We explore ensembles of hierarchical spatio-temporal trees, discovered directly from training data, to model these structures for action recognition. The hierarchical spatio-temporal trees provide a robust midlevel representation for actions. However, discovery of frequent and discriminative tree structures is challenging due to the exponential search space, particularly if one allows partial matching. We address this by first building a concise action vocabulary via discriminative clustering. Using the action vocabulary we then utilize tree mining with subsequent tree clustering and ranking to select a compact set of highly discriminative tree patterns. We show that these tree patterns, alone, or in combination with shorter patterns (action words and pairwise patterns) achieve state-of-the-art performance on two challenging datasets: UCF Sports and HighFive. Moreover, trees learned on HighFive are used in recognizing two action classes in a different dataset, Hollywood3D, demonstrating the potential for cross-dataset generality of the trees our approach discovers.
参考文献：Ma, Shugao, Leonid Sigal, and Stan Sclaroff. "Space-time tree ensemble for action recognition." Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). 2015.
报告人简介：马述高现为波士顿大学计算机系博士生，波士顿大学图像与视频计算（Image and Video Computing）实验室组员，在教授Stan Sclaroff指导下研究计算机视觉和机器学习，其主要研究课题为视频中的人类行为自动识别。他在顶级计算机视觉会议上发表多篇文章，包括CVPR，ICCV，ECCV和BMVC，并在多家公司如迪斯尼研究院，谷歌，微软和思爱普从事过计算机视觉及人工智能项目的实习。 马述高本科毕业于复旦大学，并从中科院取得硕士学位。 | 