20151112-34 林国省: Learning Deep Structured Models for Semantic Segmentation

【15-34期VALSE Webinar活动】

报告嘉宾：林国省（The University of Adelaide）
报告时间：2015年11月12日（星期四）晚21:00（北京时间）
报告题目：Learning Deep Structured Models for Semantic Segmentation. [Slides]
主持人：彭玺（A*STAR）
报告摘要：
The fist part of the talk is about how to explore the context by learning deep structured model. We achieve an intersection-over-union score of 77.8 on the challenging PASCAL VOC 2012 dataset, which is a new record.
The second part of the talk concerns a new deep structured learning method. We propose to directly learn the CNN based message estimator in message passing inference, instead of learning conventional potential functions.
参考文献：
[1.] Gushing Lin, Cfhunhua Shen, Ian Reid, Anton van dan Henge; Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation; arXiv.
[2.] Gushing Lin, Cfhunhua Shen, Ian Reid, Anton van dan Hengel;Deeply Learning the Messages in Message Passing Inference; NIPS 2015.
报告人简介：
林国省现任澳大利亚阿德莱德大学博士后研究员。2014年获阿德莱德大学博士学位，师从沈春华教授。其研究兴趣包括 Structured Learning，Deep Learning, Image retrieval, and Semantic Image Segmentation。博士期间获得Google PhD Fellowship (one of 38 winners world-wide in 2014). 目前已发表2篇 TPAMI， 2篇NIPS/ICML和4篇CVPR/ICCV/ECCV。