时间

2017年3月23日 （周四）上午11点-12点

 

地点

静园六院212会议室

 

摘要

Social media platforms (e.g., Flickr, Facebook) provide new ways for users to share their photos and experiences, generating huge amounts of multimedia resources that are available on the Internet. As reported by Flickr, the number of uploaded images reached 7.28 billion in 2015. The massive data resources have attracted a great deal of research interest in exploring real-world concepts using user-shared data, such as dense crowd, 3D objects, ecological phenomena, places of interest, storyline summarization, visual concepts, and events. The speaker focuses on event detection from Flickr-like social media by addressing the problems, including heterogeneity of the multimodal data, the low discriminative power of raw data, and the processing of streaming data. In this talk, the speaker will introduce the proposed a three-stage framework to deal with the three problems. Specifically, to address the heterogeneity problem, we propose to construct bipartite graphs based on data dictionaries. To address the low discriminative power problem, we propose a data representation learning model by incorporating four constraints: dense reconstruction error, low-rank, dictionary density inhomogeneity and local invariance. To address the streaming data, we devise a class-wise data recovery residual model by taking advantage of the rationale of data recovery. The proposed social event detection approach achieves the highest performance in terms of multiple metrics for the MediaEval Social Event Detection 2014 dataset. Finally, the speaker will give a conclusion and indicate some possible directions in this talk.

 

简历

杨振国，现为广东工业大学刘文印教授团队和香港城市大学李青教授团队博士后，2010年本科毕业于山东师范大学计算机科学与技术系，2013年硕士毕业于浙江师范大学计算机软件与理论，2017博士毕业于香港城市大学计算机科学系，主要研究领域包括：社交媒体事件检测、多模态融合、数据表征学习、迁移学习等。他在相关领域发表十几篇论文，包括ACM Transactions on Internet Technology, World Wide Web Journal等。同时是World Wide Web Journal, International Journal of Machine Learning and Cybernetics, Journal of Ambient Intelligence and Humanized Computing, Data Science and Engineering的审稿人。