行知论坛222:Fusion-Net:Supervised Deep Feature Embedding With Handcrafted Feature

时间:2019-09-30浏览:160设置


报告题目:Fusion-NetSupervised Deep Feature Embedding With Handcrafted Feature


报告人:岑翼刚教授

报告时间:2019930日(周一)10:30 - 11:30

报告地点:计算机学院4001

主办单位:计算机学院


摘要:

Image representation methods based on deep convolutional neural networks (CNNs) have achieved the state-of-the-art performance in various computer vision tasks, such as image retrieval and person re-identification. We recognize that more discriminative feature embeddings can be learned with supervised deep metric learning and handcrafted features for image retrieval and similar applications. In this paper, we propose a new supervised deep feature embedding with a handcrafted feature model. To fuse handcrafted feature information into CNNs and realize feature embeddings, a general fusion unit is proposed (called Fusion-Net). We also define a network loss function with image label information to realize supervised deep metric learning. Our extensive experimental results on the Stanford online products’ data set and the in-shop clothes retrieval data set demonstrate that our proposed methods outperform the existing state-of-the-art methods of image retrieval by a large margin. Moreover, we also explore the applications of the proposed methods in person re-identification and vehicle re-identification; the experimental results demonstrate both the effectiveness and efficiency of the proposed methods.


讲者简介:

岑翼刚,北京交通大学计算机与信息技术学院教授,博士生导师,信息科学研究所副所长。2006年毕业于武汉华中科技大学控制科学与工程系,获博士学位。2006~2007年在新加坡南洋理工大学信号处理中心博士后工作一年。2008年至今在北京交通大学计算机与信息技术学院信息科学研究所工作。20141~20151月在美国密苏里大学计算机科学系访问学习。研究领域包括:计算机视觉、压缩感知、小波构造理论及应用、数字信号、图像处理等。主持国家级、省部级项目共计17项,其中国家自然科学基金5项。累计发表学术论文90余篇,其中包括TMMTIPTCSVT在内的SCI检索论文37篇。目前担任中国图象图形学学会交通视频专委会委员兼秘书长、北京电子学会研究生教育委员会主任、国家自然科学基金通讯评审人、中国博士后基金评审专家。曾担任教育部留学回国人员基金评审专家。IEEEIET会员。TIPTCSVTIEEE Transactions on Cybernetics等多个期刊审稿人。





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