As people's aesthetic preferences for images are far from understood, image aesthetic assessment is a challenging artificial intelligence task. The range of factors underlying this task is almost unlimited, but we know that some aesthetic attributes affect those preferences. In this study, we present a multi-task convolutional neural network that takes into account these attributes. The proposed neural network jointly learns the attributes along with the overall aesthetic scores of images. This multi-task learning framework allows for effective generalization through the utilization of shared representations. Our experiments demonstrate that the proposed method outperforms the state-of-the-art approaches in predicting overall aesthetic scores for images in one benchmark of image aesthetics. We achieve near-human performance in terms of overall aesthetic scores when considering the Spearman's rank correlations. Moreover, our model pioneers the application of multi-tasking in another benchmark, serving as a new baseline for future research. Notably, our approach achieves this performance while using fewer parameters compared to existing multi-task neural networks in the literature, and consequently makes our method more efficient in terms of computational complexity.

翻译：由于人们对图像的美学偏好尚远未完全理解，图像美学评估是一项具有挑战性的人工智能任务。支撑这一任务的因素几乎无限，但我们已知某些美学属性会影响这些偏好。在本研究中，我们提出了一种考虑这些属性的多任务卷积神经网络。该网络联合学习属性与图像的整体美学评分。这种多任务学习框架通过利用共享表示实现了有效的泛化。我们的实验表明，在图像美学的一个基准测试中，所提方法在预测图像整体美学评分方面优于最先进的方法。当考虑斯皮尔曼秩相关系数时，我们在整体美学评分上达到了接近人类的性能。此外，我们的模型开创性地将多任务学习应用于另一个基准测试，为未来研究提供了新的基线。值得注意的是，与文献中现有的多任务神经网络相比，我们的方法在实现这一性能的同时使用了更少的参数，从而在计算复杂度方面更加高效。