Institute of Computing Technology, Chinese Academy IR
| Evaluating Visual Properties via Robust HodgeRank | |
| Xu, Qianqian1; Xiong, Jiechao2; Cao, Xiaochun3,4,7; Huang, Qingming1,5,6,7; Yao, Yuan8 | |
| 2021-03-04 | |
| 发表期刊 | INTERNATIONAL JOURNAL OF COMPUTER VISION
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| ISSN | 0920-5691 |
| 页码 | 22 |
| 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. |
| 关键词 | Visual properties Hodge decomposition Linearized Bregman iteration Paired comparison Robust ranking |
| DOI | 10.1007/s11263-021-01438-y |
| 收录类别 | SCI |
| 语种 | 英语 |
| 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 |
| WOS类目 | Computer Science, Artificial Intelligence |
| WOS记录号 | WOS:000625841400001 |
| 出版者 | SPRINGER |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://119.78.100.204/handle/2XEOYT63/16840 |
| 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.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing, Peoples R China 2.Tencent AI Lab, Shenzhen, 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.Hong Kong Univ Sci & Technol, Dept Math, Hong Kong, 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. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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