【15-24期VALSE Webinar活动】 报告嘉宾1：徐仲文（悉尼科技大学）
主持人：张利军（南京大学）
报告题目：A Discriminative CNN Video Representation for Event Detection [Slides]
报告时间：2015年8月5日晚20:00（北京时间）
文章信息：
[1] Z. Xu, Y. Yang and A. G. Hauptmann, A Discriminative CNN Video Representation for Event Detection, In CVPR, 2015
报告摘要：In this paper, we propose a discriminative video representation for event detection over a large scale video dataset when only limited hardware resources are available. The focus of this paper is to effectively leverage deep Convolutional Neural Networks (CNNs) to advance event detection, where only frame level static descriptors can be extracted by the existing CNN toolkits. This paper makes two contributions to the inference of CNN video representation. First, while average pooling and max pooling have long been the standard approaches to aggregating frame level static features, we show that performance can be significantly improved by taking advantage of an appropriate encoding method. Second, we propose using a set of latent concept descriptors as the frame descriptor, which enriches visual information while keeping it computationally affordable. The integration of the two contributions results in a new state-of-the-art performance in event detection over the largest video datasets. Compared to improved Dense Trajectories, which has been recognized as the best video representation for event detection, our new representation improves the Mean Average Precision (mAP) from 27.6% to 36.8% for the TRECVID MEDTest 14 dataset and from 34.0% to 44.6% for the TRECVID MEDTest 13 dataset.
报告人简介：Zhongwen Xu is a second-year Ph.D. student at Centre for Quantum Computation and Intelligent Systems (QCIS), University of Technology, Sydney (UTS), under the supervision of Dr. Yi Yang. Prior to that, he was with the He-Zhijun Honor Class in the College of Computer Science, Zhejiang University, and received his Bachelor's degree from Zhejiang University in 2013. His research interests are computer vision and multimedia, particularly in deep learning for video analysis. | 