卷积神经网络中纹理表示的感知偏差
Perceptual misalignment of texture representations in convolutional neural networks
AI总结：
本文研究了卷积神经网络中纹理表示与人类感知内容之间的对齐关系，发现传统CNN视觉模型质量评估与人类纹理感知对齐性无直接关联，表明纹理感知可能涉及不同于传统CNN对象识别模型的机制。
摘要：
视觉纹理的数学建模可以追溯到Julesz的直觉，即人类对纹理的感知基于图像特征之间的局部相关性。一种有影响力的纹理分析和生成方法将这一概念推广到卷积神经网络（CNNs）中非线性特征之间的线性相关性，这些特征被编译成Gram矩阵。鉴于CNNs常被用作视觉系统的模型，自然会问这些
英文摘要：
Mathematical modeling of visual textures traces back to Julesz's intuition that texture perception in humans is based on local correlations between image features. An influential approach for texture analysis and generation generalizes this notion to linear correlations between the nonlinear features computed by convolutional neural networks (CNNs), compiled into Gram matrices. Given that CNNs are often used as models for the visual system, it is natural to ask whether such "texture representations" spontaneously align with the textures' perceptual content, and in particular whether those CNNs that are regarded as better models for the visual system also possess more human-like texture representations. Here we quantify the perceptual content captured by feature correlations computed for a diverse pool of CNNs, and we compare it to the models' perceptual alignment with the mammalian visual system as measured by Brain-Score. Surprisingly, we find that there is no connection between conventional measures of CNN quality as a model of the visual system and its alignment with human texture perception. We conclude that texture perception involves mechanisms that are distinct from those that are commonly modeled using approaches based on CNNs trained on object recognition, possibly depending on the integration of contextual information.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
