论文标题
基于人类视觉记忆模式生成令人难忘的图像
Generating Memorable Images Based on Human Visual Memory Schemas
论文作者
论文摘要
这项研究建议使用生成的对抗网络（GAN），该网络结合了人类记忆性的二维度量，以产生令人难忘或不可存储场景的图像。通过对视觉记忆模式（VM）进行建模来评估生成图像的记忆性，该模型对应于人类观察者用来将图像编码为记忆中的心理表示。 VMS模型基于对人类观察者进行的记忆实验的结果，并提供了2D的记忆力图。我们通过使用VMS MAP预测模型作为辅助损失，对GAN的潜在空间施加了记忆性约束。我们通过独立的记忆力计算度量来评估产生的令人难忘或不可存储的图像之间的记忆性差异，并进一步评估了记忆性对生成图像的真实性的影响。
This research study proposes using Generative Adversarial Networks (GAN) that incorporate a two-dimensional measure of human memorability to generate memorable or non-memorable images of scenes. The memorability of the generated images is evaluated by modelling Visual Memory Schemas (VMS), which correspond to mental representations that human observers use to encode an image into memory. The VMS model is based upon the results of memory experiments conducted on human observers, and provides a 2D map of memorability. We impose a memorability constraint upon the latent space of a GAN by employing a VMS map prediction model as an auxiliary loss. We assess the difference in memorability between images generated to be memorable or non-memorable through an independent computational measure of memorability, and additionally assess the effect of memorability on the realness of the generated images.
