论文标题
自适应分数扩张卷积网络用于图像美学评估
Adaptive Fractional Dilated Convolution Network for Image Aesthetics Assessment
论文作者
论文摘要
为了利用深度学习来进行图像美学评估，一个关键但未解决的问题是如何无缝融合图像宽高比的信息以了解更多强大的模型。在本文中，开发了一种自适应分数扩张的卷积（AFDC），该卷积（AFDC）被开发为在卷积内核级别上本地解决此问题。具体而言，根据图像纵横比自适应地构建了分数扩张的核，其中最接近两个整数扩张的核的插值用于应对分数采样的未对准。此外，我们为小批量培训提供了简洁的配方，并利用分组策略来减少计算开销。结果，它可以通过常见的深度学习库轻松实现，并以计算有效的方式插入流行的CNN体系结构。我们的实验结果表明，我们提出的方法在AVA数据集上实现了图像美学评估的最新性能。
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.
