Fusion global and local deep representations with neural attention for aesthetic quality assessment

摘要

In recent years, deep-learning based aesthetics assessment methods have shown promising results. However, existing methods can only achieve limited success because 1) most of the methods take one fixed-size patch as the training example, which loses the fine grained details and the holistic layout information, and 2) most of the methods ignore ordinal issues in image aesthetic assessment, ie. image scored 5.3 is more likely to be in the high quality class than image scored 4.5. To address these challenges, we presents a novel convolutional networks with two branches to encode global and local features. The first branch not only captures the spatial layout information but also feedbacks the top-down neural attention. The second branch selects the important attended region to extract the fine details features. A sobel-based attention layer is integrated with the second branch to enhance fine details encoding. Regarding the second problem, we combine the strength of classification approach and regression approach by a multi-task learning framework. Extensive experiments on challenging Aesthetic and Visual Analysis (AVA) dataset and Photo.net dataset indicate the effectiveness of the proposed method.