使用深度学习和众包图像的维基百科特色网站的移动识别
Mobile Recognition of Wikipedia Featured Sites using Deep Learning and Crowd-sourced Imagery
摘要：
通过移动和增强现实媒体呈现维基百科内容可以在以城市为重点的用户社区中实现新的互动形式，促进学习，沟通和知识交流。考虑到这一目标，在这项工作中，我们开发了一个移动应用程序，允许识别维基百科上的著名网站。该应用程序由一个深度神经网络提供支持，该网络已在描述感兴趣的地点的众包图像上进行了训练，例如建筑物，雕像，博物馆或其他在城市环境中存在和视觉上可访问的物理实体。我们描述了一个端到端的管道，描述了数据汇集，模型训练和评估我们的应用程序考虑在线和现实世界的情况。我们确定了一些挑战，在网站识别任务中出现的分类网站之间的视觉相似性，以及由于周围的建筑环境引入的噪音。我们演示了如何使用移动上下文信息，如用户的位置，方向和注意力模式可以显着减轻这些挑战。此外，我们提出了一个无监督学习技术去噪的众包图像，进一步提高分类性能。
英文摘要：
Rendering Wikipedia content through mobile and augmented reality mediums can enable new forms of interaction in urban-focused user communities facilitating learning, communication and knowledge exchange. With this objective in mind, in this work we develop a mobile application that allows for the recognition of notable sites featured on Wikipedia. The application is powered by a deep neural network that has been trained on crowd-sourced imagery describing sites of interest, such as buildings, statues, museums or other physical entities that are present and visually accessible in an urban environment. We describe an end-to-end pipeline that describes data collection, model training and evaluation of our application considering online and real world scenarios. We identify a number of challenges in the site recognition task which arise due to visual similarities amongst the classified sites as well as due to noise introduce by the surrounding built environment. We demonstrate how using mobile contextual information, such as user location, orientation and attention patterns can significantly alleviate such challenges. Moreover, we present an unsupervised learning technique to de-noise crowd-sourced imagery which improves classification performance further.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
