以CNN为基础的搜索模型以简单视觉特征低估了关注指导 (CNN-based search model underestimates attention guidance by simple visual features)

Recently, Zhang et al. (2018) proposed an interesting model of attention guidance that uses visual features learnt by convolutional neural networks for object recognition. I adapted this model for search experiments with accuracy as the measure of performance. Simulation of our previously published feature and conjunction search experiments revealed that CNN-based search model considerably underestimates human attention guidance by simple visual features. A simple explanation is that the model has no bottom-up guidance of attention. Another view might be that standard CNNs do not learn features required for human-like attention guidance.

翻译：最近,张等人(2018年)提出了一个有趣的关注指导模式,该模式使用进化神经网络所学的视觉特征来识别物体。我对这个模式进行了精确的搜索实验,以此作为性能的衡量标准。模拟我们以前出版的特征和组合搜索实验表明,基于CNN的搜索模式通过简单的视觉特征大大低估了人类关注指导。一个简单的解释是,该模式没有自下而上的关注指导。另一种观点可能是,标准的CNN没有学习到像人一样的关注指导所需要的特征。