Multi-source image auto-annotation

Though the field of image auto-annotation has been extensively researched, most previous work concentrated on the single-source problem, assuming that both labelled and unseen to-be-annotated images are from a single source (e.g. an identical website), while in practice they are generally collected from multiple sources (e.g. different websites). In that case, treating each source independently may suffer from the insufficiency of labelled data for model training, while merging with labelled images from other sources can bring risky biases to the source-specific model. In this paper, we propose a multi-task learning model to alleviate the multi-source image auto-annotation problem, with each task defined as performing auto-annotation for the corresponding source. Specifically, the proposed model trains annotation models for all sources in parallel with the introduction of inter-source structure regularizers and parameter constraints for sharing information and enhancing the overall performance. Experiments conducted on three different-source benchmark datasets and their combinations yield inspiring results and demonstrate that the proposed model can well utilize the shared information and relieve the risky biases.