论文标题
哪些图像更令人难忘？
What Images are More Memorable to Machines?
论文作者
论文摘要
本文研究了衡量和预测图像对图案识别机的难忘方式的问题，这是探索机器智能的途径。首先，我们提出了一个自我监督的机器存储器定量管道，称为``Machinemem Measure''，以收集机器的记忆力分数。与人类类似，机器也倾向于记住某些类型的图像，而机器和人类记忆的图像类型则不同。通过深入分析和全面的可视化，我们逐渐揭露了“复合”图像通常更令人难忘。我们进一步进行了11种不同的机器（从线性分类器到现代VITS）和9种预训练的方法，以及分析和理解机器记忆的9种预训练方法。这项工作能够使机器的记忆力和开放的计算机构成新的研究界面。
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 memorize 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.
