Placepedia：全面的地方理解与多方面的注释
Placepedia: Comprehensive Place Understanding with Multi-Faceted Annotations
摘要：
地点是视觉理解中的一个重要元素。给定一个建筑物的照片，人们通常可以告诉它的功能，例如餐厅或商店，它的文化风格，例如亚洲或欧洲，以及它的经济类型，例如工业导向或旅游导向。虽然地点识别在以前的工作中已经得到了广泛的研究，但要实现全面的地点理解还有很长的路要走，这远远超出了用图像对地点进行分类，需要多方面的信息。在这项工作中，我们贡献了Placepedia，这是一个大规模的地方数据集，拥有来自240 K独特地方的超过3500万张照片。除了照片，每个地方还附带了大量多方面的信息，如GDP，人口等，以及多个级别的标签，包括功能、城市、国家等。该数据集拥有大量数据和丰富的注释，可以进行各种研究。特别是，在我们的研究中，我们开发了1）PlaceNet，一个多层次地点识别的统一框架，以及2）一种城市嵌入方法，它可以为城市生成一个矢量表示，捕获视觉和多方面的辅助信息。这些研究不仅揭示了地方理解的关键挑战，而且还建立了视觉观察和潜在的社会经济/文化影响之间的联系。
英文摘要：
Place is an important element in visual understanding. Given a photo of a building, people can often tell its functionality, e.g. a restaurant or a shop, its cultural style, e.g. Asian or European, as well as its economic type, e.g. industry oriented or tourism oriented. While place recognition has been widely studied in previous work, there remains a long way towards comprehensive place understanding, which is far beyond categorizing a place with an image and requires information of multiple aspects. In this work, we contribute Placepedia, a large-scale place dataset with more than 35M photos from 240K unique places. Besides the photos, each place also comes with massive multi-faceted information, e.g. GDP, population, etc., and labels at multiple levels, including function, city, country, etc.. This dataset, with its large amount of data and rich annotations, allows various studies to be conducted. Particularly, in our studies, we develop 1) PlaceNet, a unified framework for multi-level place recognition, and 2) a method for city embedding, which can produce a vector representation for a city that captures both visual and multi-faceted side information. Such studies not only reveal key challenges in place understanding, but also establish connections between visual observations and underlying socioeconomic/cultural implications.
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。
