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
this https URL

本文研究了计算机视觉中的裁剪操作对图像质量的影响，特别是对于色彩像差和暗角等图像瑕疵的快速检测与矫正，并探讨了其对神经网络学习的影响。

图像裁剪解析

Dissecting Image Crops

Deep Reinforcement Learning (DRL) has shown impressive performance on domains
with visual inputs, in particular various games. However, the agent is usually
trained on a fixed environment, e.g. a fixed number of levels. A growing mass
of evidence suggests that these trained models fail to generalize to even
slight variations of the environments they were trained on. This paper advances
the hypothesis that the lack of generalization is partly due to the input
representation, and explores how rotation, cropping and translation could
increase generality. We show that a cropped, translated and rotated observation
can get better generalization on unseen levels of two-dimensional arcade games
from the GVGAI framework. The generality of the agents is evaluated on both
human-designed and procedurally generated levels.

本文针对 Deep Reinforcement Learning 在视觉输入的情况下表现出色，但是通常训练时在固定的环境下训练，缺乏泛化能力，提出了使用 rotation 和 cropping 等方法来提高泛化能力，并在两个维度的街机游戏中进行了验证。