  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.
