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人群的多维智慧

The Multidimensional Wisdom of Crowds
课程网址: http://videolectures.net/nips2010_welinder_mwc/  
主讲教师: Peter Welinder
开课单位: 加州理工学院
开课时间: 2011-01-12
课程语种: 英语
中文简介:
在成百上千的注释器之间分布标记任务是注释大型数据集的一种日益重要的方法。我们提出了一种从多个注释器提供的(噪声)注释中估计每个图像的底层值(例如类)的方法。我们的方法是基于图像形成和注释过程的模型。每个图像都有不同的特征,这些特征在抽象欧几里得空间中表示。每个注释员都被建模为一个多维实体,变量代表能力、专业知识和偏见。这使得模型能够发现和表示具有不同技能和知识集的注释器组,以及定性上不同的图像组。我们发现,我们的模型预测合成和真实数据上的地面实况标签比最先进的方法更准确。实验还表明,我们的模型从一组二进制标签开始,可以发现丰富的信息,例如注释者之间的不同思想流派,并可以将属于不同类别的图像分组。
课程简介: Distributing labeling tasks among hundreds or thousands of annotators is an increasingly important method for annotating large datasets. We present a method for estimating the underlying value (e.g. the class) of each image from (noisy) annotations provided by multiple annotators. Our method is based on a model of the image formation and annotation process. Each image has different characteristics that are represented in an abstract Euclidean space. Each annotator is modeled as a multidimensional entity with variables representing competence, expertise and bias. This allows the model to discover and represent groups of annotators that have different sets of skills and knowledge, as well as groups of images that differ qualitatively. We find that our model predicts ground truth labels on both synthetic and real data more accurately than state of the art methods. Experiments also show that our model, starting from a set of binary labels, may discover rich information, such as different "schools of thought" amongst the annotators, and can group together images belonging to separate categories.
关 键 词: 计算机科学; 数据挖掘; 多维信号
课程来源: 视频讲座网
最后编审: 2020-06-02:毛岱琦(课程编辑志愿者)
阅读次数: 53