Automatic image cropping algorithms aim to recompose images like human-being photographers by generating the cropping boxes with improved composition quality. Cropping box regression approaches learn the beauty of composition from annotated cropping boxes. However, the bias of annotations leads to quasi-trivial recomposing results, which has an obvious tendency to the average location of training samples. The crux of this predicament is that the task is naively treated as a box regression problem, where rare samples might be dominated by normal samples, and the composition patterns of rare samples are not well exploited. Observing that similar composition patterns tend to be shared by the cropping boundaries annotated nearly, we argue to find the beauty of composition from the rare samples by clustering the samples with similar cropping boundary annotations, ie, similar composition patterns. We propose a novel Contrastive Composition Clustering (C2C) to regularize the composition features by contrasting dynamically established similar and dissimilar pairs. In this way, common composition patterns of multiple images can be better summarized, which especially benefits the rare samples and endows our model with better generalizability to render nontrivial results. Extensive experimental results show the superiority of our model compared with prior arts. We also illustrate the philosophy of our design with an interesting analytical visualization.

翻译：自动图像裁剪算法旨在通过生成构图质量更佳的裁剪框，像人类摄影师一样对图像进行重新构图。裁剪框回归方法从带标注的裁剪框中学习构图美学。然而，标注偏差会导致准平庸的重新构图结果，其明显偏向于训练样本的平均位置。这一困境的关键在于该任务被朴素地视为一个框回归问题，其中稀有样本可能被常规样本主导，且稀有样本的构图模式未被充分利用。观察到相似的构图模式往往由近似标注的裁剪边界共享，我们主张通过将具有相似裁剪边界标注（即相似构图模式）的样本进行聚类，从稀有样本中发掘构图之美。我们提出了一种新颖的对比构图聚类方法（C2C），通过对比动态建立的相似与不相似对来正则化构图特征。通过这种方式，多张图像的共同构图模式得以更好地归纳，这对稀有样本尤其有益，并赋予模型更强的泛化能力以生成非平凡结果。大量实验结果表明，与先前方法相比，我们的模型具有优越性。我们还通过有趣的可视化分析展示了我们设计的思想。