图像记忆预测研究:从传统模型到深度学习模型

Souad Lahrache, Rajae El Ouazzani
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引用次数: 1

摘要

图像可记忆性预测是一项有趣且具有挑战性的任务,其目的是创建可记忆图像的代表性模型,以便从其他图像中识别可记忆图像。实际上,可记忆性在不同的领域和不同的应用中都很有价值。近年来,图像可记忆性在计算机视觉和机器学习社区引起了相当大的兴趣。人们提出了不同的方法,这些方法对记忆性分析的性能产生了重大影响。因此,本文的目的是揭示用于预测和分析图像记忆性的不同方法和技术。因此,我们公开并比较用于性能记忆性估计的数据集和评估指标。然后,我们讨论了图像记忆预测改进的许多挑战和机遇,为未来提供一些指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Survey on Image Memorability Prediction: From Traditional to Deep Learning Models
Image memorability prediction is an interesting and challenging task which aims to create a representative model of memorable images, in order to recognize memorable ones from others. Actually, memorability can be valuable in different areas and several applications. In recent years, image memorability has received considerable interest in computer vision and machine learning communities. Different approaches have been proposed which have made a significant impact on memorability analysis performances. Thus, the purpose of this paper is to expose different methods and techniques used to predict and analyze image memorability. Therefore, we expose and compare datasets and evaluation metrics used for performance memorability estimation. Then, we discuss many challenges and opportunities for image memorability prediction improvement, which can provide some future guidelines.
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