20150805-24 常晓军：Complex Event Detection using Semantic Saliency...
摘要: 【15-24期VALSE Webinar活动】报告嘉宾2：常晓军（悉尼科技大学）主持人：张利军（南京大学）报告题目：Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM 报告时间：2015年8月5日晚20:40（ ...
【15-24期VALSE Webinar活动】 报告嘉宾2：常晓军（悉尼科技大学）主持人：张利军（南京大学） 报告题目：Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM [Slides] 报告时间：2015年8月5日晚20:40（北京时间） 文章信息： [1] X. Chang, Y. Yang, E. P. Xing, and Y. Yu. Proceedings of the 32nd International Conference on Machine Learning (ICML), 2015. 报告摘要：We aim to detect complex events in long Internet videos that may last for hours. A major challenge in this setting is that only a few shots in a long video are relevant to the event of interest while others are irrelevant or even misleading. Instead of indifferently pooling the shots, we first define a novel notion of semantic saliency that assesses the relevance of each shot with the event of interest. We then prioritize the shots according to their saliency scores since shots that are semantically more salient are expected to contribute more to the final event detector. Next, we propose a new isotonic regularizer that is able to exploit the semantic ordering information. The resulting nearly-isotonic SVM classifier exhibits higher discriminative power. Computationally, we develop an efficient implementation using the proximal gradient algorithm, and we prove new, closed-form proximal steps. We conduct extensive experiments on three real-world video datasets and confirm the effectiveness of the proposed approach. 报告人简介：Xiaojun Chang is a Ph.D student at Centre for Quantum Computation and Intelligent System (QCIS), University of Technology, Sydney, under the supervision of Dr. Yi Yang. His research interests are machine learning and its applications to multimedia and computer vision. |
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