  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.
