论文标题
CNN和变形金刚感知的混合图像类似于人类
CNNs and Transformers Perceive Hybrid Images Similar to Humans
论文作者
论文摘要
混合图像是一种生成具有两个解释的图像的技术，这些解释会随着观看距离而变化。它已用于研究人类视觉系统对图像的多尺度处理。在10种水果类别中，使用63,000张混合图像，在这里我们表明，深度学习视觉模型的预测与人类对这些图像的看法有定性匹配。我们的结果提供了另一个证据，以支持卷积神经网络（CNN）和变形金刚擅长建模视觉皮层腹侧信息的进料扫描的假设。代码和数据可从https://github.com/aliborji/hybrid_images.git获得。
Hybrid images is a technique to generate images with two interpretations that change as a function of viewing distance. It has been utilized to study multiscale processing of images by the human visual system. Using 63,000 hybrid images across 10 fruit categories, here we show that predictions of deep learning vision models qualitatively matches with the human perception of these images. Our results provide yet another evidence in support of the hypothesis that Convolutional Neural Networks (CNNs) and Transformers are good at modeling the feedforward sweep of information in the ventral stream of visual cortex. Code and data is available at https://github.com/aliborji/hybrid_images.git.
