  Online personalized news product needs a suitable cover for the article. The
news cover demands to be with high image quality, and draw readers' attention
at same time, which is extraordinary challenging due to the subjectivity of the
task. In this paper, we assess the news cover from image clarity and object
salience perspective. We propose an end-to-end multi-task learning network for
image clarity assessment and semantic segmentation simultaneously, the results
of which can be guided for news cover assessment. The proposed network is based
on a modified DeepLabv3+ model. The network backbone is used for multiple scale
spatial features exaction, followed by two branches for image clarity
assessment and semantic segmentation, respectively. The experiment results show
that the proposed model is able to capture important content in images and
performs better than single-task learning baselines on our proposed game
content based CIA dataset.
