  As it is said by Van Gogh, great things are done by a series of small things
brought together. Aesthetic experience arises from the aggregation of
underlying visual components. However, most existing deep image aesthetic
assessment (IAA) methods over-simplify the IAA process by failing to model
image aesthetics with clearly-defined visual components as building blocks. As
a result, the connection between resulting aesthetic predictions and underlying
visual components is mostly invisible and hard to be explicitly controlled,
which limits the model in both performance and interpretability. This work aims
to model image aesthetics from the level of visual components. Specifically,
object-level regions detected by a generic object detector are defined as
visual components, namely object-level visual components (OVCs). Then generic
features representing OVCs are aggregated for the aesthetic prediction based
upon proposed object-level and graph attention mechanisms, which dynamically
determines the importance of individual OVCs and relevance between OVC pairs,
respectively. Experimental results confirm the superiority of our framework
over previous relevant methods in terms of SRCC and PLCC on the aesthetic
rating distribution prediction. Besides, quantitative analysis is done towards
model interpretation by observing how OVCs contribute to aesthetic predictions,
whose results are found to be supported by psychology on aesthetics and
photography rules. To the best of our knowledge, this is the first attempt at
the interpretation of a deep IAA model.
