论文标题
学习视觉外观的动态图
Learning a Dynamic Map of Visual Appearance
论文作者
论文摘要
世界的出现不仅在一个地方到处都有很大的变化，而且从一个小时到一个月也是如此。每天的数十亿图像捕获了这种复杂的关系，其中许多与精确的时间和位置元数据有关。我们建议使用这些图像来构建视觉外观属性的全局尺度，动态图。这样的地图使对任何地理位置和时间上预期外观的细粒度了解。我们的方法将密集的高架图像与位置和时间元数据集成到一个能够绘制各种视觉属性的通用框架中。我们方法的关键特征是它不需要手动数据注释。我们演示了这种方法如何支持各种应用程序，包括图像驱动的映射，图像地理定位和元数据验证。
The appearance of the world varies dramatically not only from place to place but also from hour to hour and month to month. Every day billions of images capture this complex relationship, many of which are associated with precise time and location metadata. We propose to use these images to construct a global-scale, dynamic map of visual appearance attributes. Such a map enables fine-grained understanding of the expected appearance at any geographic location and time. Our approach integrates dense overhead imagery with location and time metadata into a general framework capable of mapping a wide variety of visual attributes. A key feature of our approach is that it requires no manual data annotation. We demonstrate how this approach can support various applications, including image-driven mapping, image geolocalization, and metadata verification.
