Abstract: We propose Mixed-Precision Multibranch Network (M+MNet) to compensate for the neglect of background information in image aesthetics assessment (IAA) while providing strategies for overcoming the dilemma between training costs and performance.  First, two exponentially weighted pooling methods are used to selectively boost the extraction of background and salient information during downsampling. Second, we propose Corner Grid, an unsupervised data augmentation method that leverages the diffusive characteristics of convolution to force the network to seek more relevant background information. Third, we perform mixed-precision training by switching the precision format, thus significantly reducing the time and memory consumption of data representation and transmission. Most of our methods specifically designed for IAA tasks have demonstrated generalizability to other IAA works. For performance verification, we develop a large-scale benchmark (the most comprehensive thus far) by comparing 17 methods with M+MNet on two representative datasets: the Aesthetic Visual Analysis (AVA) dataset and FLICKR-Aesthetic Evaluation Subset (FLICKR-AES). M+MNet achieves state-of-the-art performance on all tasks.

Keywords: deep learning; image aesthetics assessment; multibranch network