Multimodal Image Aesthetic Prediction with Missing Modality

With the increasing growth of multimedia data on the Internet, multimodal image aesthetic assessment has attracted a great deal of attention in the image processing community. However, traditional multimodal methods often have the following two problems: (1) Existing multimodal image aesthetic methods are based on the assumption that full modalities are available in all samples, which is unapplicable in most cases since textual information is more difficult to obtain. (2) They only fuse multimodal information at a single level and ignore their interaction at different levels. To address these two challenges, we proposed a novel framework termed Missing-Modility-Multimodal-Bert networks (MMMB). To achieve the completeness, we first generate the missing textual modality conditioned on the available visual modality. We then project the image features to the token space of the text, and use the transformer’s self-attention mechanism to make the two different modalities information interact at different levels for earlier and more fine-grained fusion, rather than only at the final layer. A large number of experiments on two large benchmark datasets in the field of image aesthetic quality evaluation: AVA and Photo.net demonstrate that the proposed model significantly improves image aesthetic assessment performance under both textual missing modality condition and full-modality condition.