Beyond universal saliency: Personalized Saliency prediction with multi-task CNN

	Beyond universal saliency: Personalized Saliency prediction with multi-task CNN
	Xu, Yanyu1 ; Li, Nianyi 2,3; Wu, Junru1 ; Yu, Jingyi1,3 ; Gao, Shenghua1
	2017
会议录名称	26TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, IJCAI 2017
页码	3887-3893
发表状态	已发表
摘要	Saliency detection is a long standing problem in computer vision. Tremendous efforts have been focused on exploring a universal saliency model across users despite their differences in gender, race, age, etc. Yet recent psychology studies suggest that saliency is highly specific than universal: individuals exhibit heterogeneous gaze patterns when viewing an identical scene containing multiple salient objects. In this paper, we first show that such heterogeneity is common and critical for reliable saliency prediction. Our study also produces the first database of personalized saliency maps (PSMs). We model PSM based on universal saliency map (USM) shared by different participants and adopt a multitask CNN framework to estimate the discrepancy between PSM and USM. Comprehensive experiments demonstrate that our new PSM model and prediction scheme are effective and reliable.
会议地点	Melbourne, VIC, Australia
收录类别	EI
资助项目	National Natural Science Foundation of China[61502304]
出版者	International Joint Conferences on Artificial Intelligence
EI入藏号	20174304308633
EI主题词	Artificial intelligence ; Image segmentation
EI分类号	Artificial Intelligence:723.4
原始文献类型	Conference article (CA)
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/13340
专题	信息科学与技术学院信息科学与技术学院_PI研究组_虞晶怡组信息科学与技术学院_PI研究组_高盛华组信息科学与技术学院_博士生
通讯作者	Gao, Shenghua
作者单位	1.ShanghaiTech University, Shanghai, China 2.University of Delaware, Newark; DE, United States 3.Plex-VR Digital Technology Co., Ltd., United States
第一作者单位	上海科技大学
通讯作者单位	上海科技大学
第一作者的第一单位	上海科技大学
推荐引用方式 GB/T 7714	Xu, Yanyu,Li, Nianyi,Wu, Junru,et al. Beyond universal saliency: Personalized Saliency prediction with multi-task CNN[C]:International Joint Conferences on Artificial Intelligence,2017:3887-3893.