报告题目: Hierarchically Gated Deep Networks for Semantic Segmentation

报告人：齐国君 博士

单位：美国中佛罗里达大学计算机系

报告时间：2016年7月11日（周一）上午10:00-11:00

报告地点：逸夫科教楼408会议室

报告摘要: Semantic segmentation aims to parse the scene structure of images by annotating the labels to each pixel so that images can be segmented into different regions. While image structures usually have various scales, it is difficult to use a single scale to model the spatial contexts for all

individual pixels. Multi-scale Convolutional Neural Networks (CNNs) and their variants have made striking success for modeling the global scene structure for an image. However, they are limited in labeling fine-grained local structures like pixels and patches, since spatial contexts might be blindly mixed up without appropriately customizing their scales. To address this challenge, we develop a novel paradigm of multi-scale deep network to model spatial contexts surrounding different pixels at various scales. It builds multiple layers of memory cells, learning feature representations for individual pixels at their customized scales by hierarchically absorbing relevant spatial contexts via memory gates between layers. Such Hierarchically Gated Deep Networks (HGDNs) can customize a suitable scale for each pixel, thereby delivering better performance on labeling scene structures of various scales. We conduct the experiments on two datasets, and show competitive results compared with the other multi-scale deep networks on the semantic segmentation task.

报告人简介：齐国君博士是美国中佛罗里达大学计算机系助理教授。齐博士研究兴趣包括面向多源异构大数据的数据挖掘、智能信息处理等，并将所提出的方法应用于社交网络、医疗健康、金融系统等多个领域之中。齐博士在包括Preceedings of IEEE、TPAMI、TKDE、TIP、SIGKDD、ICML、CVPR、MM、WWW、ICDE、ICDM等众多顶级期刊和会议发表超过六十篇论文，被引用超过2500次，H-index为24。齐博士获得过ICDM2014最佳学生论文奖、ICDE2013最佳论文奖、MM2007最佳论文奖、两次IBM学者奖、一次微软学者奖。齐博士担任了MMM2016大会共同主席，SIGKDD、CIKM、MM等多个顶级会议的领域主席，以及CVPR、ICCV等顶级会议的程序委员会委员。齐博士亦是IEEE Trans. Big Data、IEEE Trans. Multimedia等顶级期刊责任客座编委。

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