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Evaluating Visual Properties via Robust HodgeRank

文献类型：期刊论文

作者Xu, Qianqian2; Xiong, Jiechao8; Cao, Xiaochun3,4,7; Huang, Qingming2,5,6,7; Yao, Yuan1
刊名INTERNATIONAL JOURNAL OF COMPUTER VISION
出版日期2021-03-04
页码22
关键词Visual properties Hodge decomposition Linearized Bregman iteration Paired comparison Robust ranking
ISSN号0920-5691
DOI10.1007/s11263-021-01438-y
英文摘要Nowadays, how to effectively evaluate visual properties has become a popular topic for fine-grained visual comprehension. In this paper we study the problem of how to estimate such visual properties from a ranking perspective with the help of the annotators from online crowdsourcing platforms. The main challenges of our task are two-fold. On one hand, the annotations often contain contaminated information, where a small fraction of label flips might ruin the global ranking of the whole dataset. On the other hand, considering the large data capacity, the annotations are often far from being complete. What is worse, there might even exist imbalanced annotations where a small subset of samples are frequently annotated. Facing such challenges, we propose a robust ranking framework based on the principle of Hodge decomposition of imbalanced and incomplete ranking data. According to the HodgeRank theory, we find that the major source of the contamination comes from the cyclic ranking component of the Hodge decomposition. This leads us to an outlier detection formulation as sparse approximations of the cyclic ranking projection. Taking a step further, it facilitates a novel outlier detection model as Huber's LASSO in robust statistics. Moreover, simple yet scalable algorithms are developed based on Linearized Bregman Iteration to achieve an even less biased estimator. Statistical consistency of outlier detection is established in both cases under nearly the same conditions. Our studies are supported by experiments with both simulated examples and real-world data. The proposed framework provides us a promising tool for robust ranking with large scale crowdsourcing data arising from computer vision.
资助项目National Key R&D Program of China[2018AAA0102003] ; National Natural Science Foundation of China[61861166002] ; National Natural Science Foundation of China[U1736219] ; National Natural Science Foundation of China[61976202] ; National Natural Science Foundation of China[U1803264] ; National Natural Science Foundation of China[61620106009] ; National Natural Science Foundation of China[61931008] ; National Natural Science Foundation of China[61836002] ; Youth Innovation Promotion Association CAS ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB28000000] ; Hong Kong Research Grant Council (HKRGC)[16303817] ; Hong Kong Research Grant Council (HKRGC)[ITF UIM/390] ; Tencent AI Lab ; Si Family Foundation ; Microsoft Research-Asia
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000625841400001
出版者SPRINGER
源URL[http://119.78.100.204/handle/2XEOYT63/16839]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Xu, Qianqian; Yao, Yuan
作者单位1.Hong Kong Univ Sci & Technol, Dept Math, Hong Kong, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Informat Engn, State Key Lab Informat Secur, Beijing, Peoples R China
4.Univ Chinese Acad Sci, Sch Cyber Secur, Beijing, Peoples R China
5.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing, Peoples R China
6.Univ Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing, Peoples R China
7.Peng Cheng Lab, Shenzhen, Peoples R China
8.Tencent AI Lab, Shenzhen, Peoples R China
推荐引用方式
GB/T 7714		 Xu, Qianqian,Xiong, Jiechao,Cao, Xiaochun,et al. Evaluating Visual Properties via Robust HodgeRank[J]. INTERNATIONAL JOURNAL OF COMPUTER VISION,2021:22.
APA Xu, Qianqian,Xiong, Jiechao,Cao, Xiaochun,Huang, Qingming,&Yao, Yuan.(2021).Evaluating Visual Properties via Robust HodgeRank.INTERNATIONAL JOURNAL OF COMPUTER VISION,22.
MLA Xu, Qianqian,et al."Evaluating Visual Properties via Robust HodgeRank".INTERNATIONAL JOURNAL OF COMPUTER VISION (2021):22.

入库方式： OAI收割

来源：计算技术研究所

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