超越可见场景

A. Khosla, Byoungkwon An, Joseph J. Lim, A. Torralba
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引用次数: 72

摘要

将许多先前的场景理解工作联系在一起的一个共同线索是,他们关注场景中直接存在的方面,如其分类或对象集。在这项工作中,我们建议超越场景的可见元素;我们证明了一个场景不仅仅是对象及其配置或分配给其像素的标签的集合-它是如此之多。通过对现场的简单观察,我们可以了解现场周围的环境,例如附近的潜在场所,该地区的潜在犯罪率,甚至是经济气候。在这里,我们从人类感知和计算机视觉的角度探讨了其中的几个方面。具体来说,我们表明,即使使用远离麦当劳或医院的场景,也可以预测周围场所的距离。我们进一步表明,人类和计算机在仅基于场景的视觉线索导航环境方面表现良好。最后,我们表明,在没有任何实时犯罪活动的情况下,仅仅通过观察现场就可以预测一个地区的犯罪率。简单地说,在这里,我们说明了超越可见场景的观察是可能的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Looking Beyond the Visible Scene
A common thread that ties together many prior works in scene understanding is their focus on the aspects directly present in a scene such as its categorical classification or the set of objects. In this work, we propose to look beyond the visible elements of a scene; we demonstrate that a scene is not just a collection of objects and their configuration or the labels assigned to its pixels - it is so much more. From a simple observation of a scene, we can tell a lot about the environment surrounding the scene such as the potential establishments near it, the potential crime rate in the area, or even the economic climate. Here, we explore several of these aspects from both the human perception and computer vision perspective. Specifically, we show that it is possible to predict the distance of surrounding establishments such as McDonald's or hospitals even by using scenes located far from them. We go a step further to show that both humans and computers perform well at navigating the environment based only on visual cues from scenes. Lastly, we show that it is possible to predict the crime rates in an area simply by looking at a scene without any real-time criminal activity. Simply put, here, we illustrate that it is possible to look beyond the visible scene.
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