Deep-Crowd-Label: A deep-learning based crowd-assisted system for location labeling

Mohammad-Mahdi Moazzami, Jasvinder Singh, Vijay Srinivasan, G. Xing
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Abstract

Semantic labels are crucial parts of many location-based applications. Previous efforts in location-based systems have mostly paid attention to achieve high accuracy in localization or navigation, with the assumption that the mapping between the locations and the semantic labels are given or will be done manually. In this paper, we propose a system called Deep-Crowd-Label that automatically assigns semantic labels to locations. We propose a novel transfer learning method that leverages deep learning models deployed on many crowd-workers to assign semantic labels to locations by classifying associated visual data. Deep-Crowd-Label uses the power of the crowd to aggregate the individual predictions done by the model across the crowd-workers visiting the same location. Our preliminary experiments with 26 different types of locations show that, our method and our prototype system is able to find the right label for the locations i.e., coffee shop to the Starbucks.
Deep-Crowd-Label:一种基于深度学习的人群辅助定位标记系统
语义标签是许多基于位置的应用程序的关键部分。在基于位置的系统中,以前的工作主要集中在实现定位或导航的高精度上,并且假设位置和语义标签之间的映射是给定的或将手动完成的。在本文中,我们提出了一个称为Deep-Crowd-Label的系统,该系统自动为位置分配语义标签。我们提出了一种新的迁移学习方法,该方法利用部署在许多人群工作人员身上的深度学习模型,通过分类相关的视觉数据来为位置分配语义标签。Deep-Crowd-Label利用人群的力量,在访问同一地点的人群中汇总模型所做的个人预测。我们对26个不同类型的地点进行的初步实验表明,我们的方法和原型系统能够为地点找到正确的标签,即从咖啡店到星巴克。
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