  With the proliferation of various gaming technology, services, game styles,
and platforms, multi-dimensional aesthetic assessment of the gaming contents is
becoming more and more important for the gaming industry. Depending on the
diverse needs of diversified game players, game designers, graphical
developers, etc. in particular conditions, multi-modal aesthetic assessment is
required to consider different aesthetic dimensions/perspectives. Since there
are different underlying relationships between different aesthetic dimensions,
e.g., between the `Colorfulness' and `Color Harmony', it could be advantageous
to leverage effective information attached in multiple relevant dimensions. To
this end, we solve this problem via multi-task learning. Our inclination is to
seek and learn the correlations between different aesthetic relevant dimensions
to further boost the generalization performance in predicting all the aesthetic
dimensions. Therefore, the `bottleneck' of obtaining good predictions with
limited labeled data for one individual dimension could be unplugged by
harnessing complementary sources of other dimensions, i.e., augment the
training data indirectly by sharing training information across dimensions.
According to experimental results, the proposed model outperforms
state-of-the-art aesthetic metrics significantly in predicting four gaming
aesthetic dimensions.
