Learning Multi-Scale Attentive Features for Series Photo Selection

People used to take a series of nearly identical photos about the same subject, but it is usually a tedious chore to select the reversed ones from them. Despite the remarkable progress, most existing studies on image aesthetics assessment fail to fulfill the task of series photo selection. In this paper, we develop a novel deep CNN architecture that aggregates multi-scale features from different network layers, in order to capture the subtle differences between series photos. To reduce the risk of redundant or even interfering features, we introduce the spatial-channel self-attention mechanism to adaptively recalibrate the features at each layer, so that informative features can be selectively emphasized and less useful ones suppressed. Extensive experiments on a benchmark dataset well demonstrate the potential of our approach for series photo selection.