To leverage deep learning for image aesthetics assessment, one critical but
unsolved issue is how to seamlessly incorporate the information of image aspect
ratios to learn more robust models. In this paper, an adaptive fractional
dilated convolution (AFDC), which is aspect-ratio-embedded,
composition-preserving and parameter-free, is developed to tackle this issue
natively in convolutional kernel level. Specifically, the fractional dilated
kernel is adaptively constructed according to the image aspect ratios, where
the interpolation of nearest two integers dilated kernels is used to cope with
the misalignment of fractional sampling. Moreover, we provide a concise
formulation for mini-batch training and utilize a grouping strategy to reduce
computational overhead. As a result, it can be easily implemented by common
deep learning libraries and plugged into popular CNN architectures in a
computation-efficient manner. Our experimental results demonstrate that our
proposed method achieves state-of-the-art performance on image aesthetics
assessment over the AVA dataset.

本文提出了一种自适应分数膨胀卷积（AFDC）方法，采用图像宽高比嵌入、保持组成以及无需额外参数的方式在卷积内核层面处理图像宽高比信息，通过使用最近两个整数膨胀卷积核内插可以解决分数采样对齐问题；并提供了迷你批量训练的简洁公式，并利用分组策略来减少计算开销。实验结果表明，该方法在 AVA 数据集上的图像美学评估方面达到了最先进的性能。