论文标题
解剖图像作物
Dissecting Image Crops
论文作者
论文摘要
作物的基本操作几乎为每个计算机视觉系统的基础，从数据增强和翻译不变性到计算摄影和表示学习。本文研究了此操作引入的微妙痕迹。例如，尽管对相机光学元件进行了改进，但镜头仍会留下某些线索，特别是色差和渐晕。摄影师还留下了与图像美学和场景构图有关的其他线索。我们研究了如何检测这些痕迹，并研究了农作物对图像分布的影响。尽管我们的目的是剖析空间作物的基本影响，但对我们的工作也有许多实际的影响，例如揭示摄影作品错误，并为神经网络研究人员提供对快捷方式学习的更好理解。代码可从https://github.com/basilevh/dissecting-image-crops获得。
The elementary operation of cropping underpins nearly every computer vision system, ranging from data augmentation and translation invariance to computational photography and representation learning. This paper investigates the subtle traces introduced by this operation. For example, despite refinements to camera optics, lenses will leave behind certain clues, notably chromatic aberration and vignetting. Photographers also leave behind other clues relating to image aesthetics and scene composition. We study how to detect these traces, and investigate the impact that cropping has on the image distribution. While our aim is to dissect the fundamental impact of spatial crops, there are also a number of practical implications to our work, such as revealing faulty photojournalism and equipping neural network researchers with a better understanding of shortcut learning. Code is available at https://github.com/basilevh/dissecting-image-crops.
