  Image-guided story ending generation (IgSEG) is to generate a story ending
based on given story plots and ending image. Existing methods focus on
cross-modal feature fusion but overlook reasoning and mining implicit
information from story plots and ending image. To tackle this drawback, we
propose a multimodal event transformer, an event-based reasoning framework for
IgSEG. Specifically, we construct visual and semantic event graphs from story
plots and ending image, and leverage event-based reasoning to reason and mine
implicit information in a single modality. Next, we connect visual and semantic
event graphs and utilize cross-modal fusion to integrate different-modality
features. In addition, we propose a multimodal injector to adaptive pass
essential information to decoder. Besides, we present an incoherence detection
to enhance the understanding context of a story plot and the robustness of
graph modeling for our model. Experimental results show that our method
achieves state-of-the-art performance for the image-guided story ending
generation.
