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Evaluating Visual Properties via Robust HodgeRank
International Journal of Computer Vision ( IF 19.5 ) Pub Date : 2021-03-04 , DOI: 10.1007/s11263-021-01438-y
Qianqian Xu , Jiechao Xiong , Xiaochun Cao , Qingming Huang , Yuan Yao

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.



中文翻译:

通过健壮的HodgeRank评估视觉属性

如今,如何有效地评估视觉属性已成为细粒度视觉理解的热门话题。在本文中,我们借助在线众包平台的注释者,研究了如何从排名角度评估此类视觉属性的问题。我们任务的主要挑战是双重的。一方面,注释通常包含受污染的信息,一小部分标签翻转可能会破坏整个数据集的整体排名。另一方面,考虑到大数据容量,注释通常远远不够完整。更糟糕的是,甚至可能存在不平衡的注释,其中一小部分样本经常被注释。面对这样的挑战,我们基于不平衡和不完整排名数据的Hodge分解原理,提出了一个稳健的排名框架。根据HodgeRank理论,我们发现污染的主要来源来自Hodge分解的循环等级成分。这导致我们提出了一种异常检测公式,即循环等级预测的稀疏近似。更进一步,它为鲁棒统计中的Huber LASSO提供了一种新颖的异常值检测模型。此外,基于线性Bregman迭代开发了简单而可扩展的算法,以实现偏差更小的估计量。在几乎相同的条件下,两种情况都建立了异常值检测的统计一致性。我们的研究得到了模拟实例和真实数据的实验支持。

更新日期:2021-03-04
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