This paper studies the problem of measuring and predicting how memorable an
image is to pattern recognition machines, as a path to explore machine
intelligence. Firstly, we propose a self-supervised machine memory
quantification pipeline, dubbed ``MachineMem measurer'', to collect machine
memorability scores of images. Similar to humans, machines also tend to
memorize certain kinds of images, whereas the types of images that machines and
humans memorialize are different. Through in-depth analysis and comprehensive
visualizations, we gradually unveil that "complex" images are usually more
memorable to machines. We further conduct extensive experiments across 11
different machines (from linear classifiers to modern ViTs) and 9 pre-training
methods to analyze and understand machine memory. This work proposes the
concept of machine memorability and opens a new research direction at the
interface between machine memory and visual data.

本文研究了计算机视觉中图像记忆度的量化和预测问题，提出了机器记忆度和复杂度的概念，并分析了机器和人类在图像记忆方面的不同表现和机器记忆的影响因素。