Title: CLMIU: Commonsense Learning in Multimodal Image Understanding.. Abstract: The problem of automatically describing the content of an image through accurate and meaningful captions has been attracting considerable attention among computer vision researchers. Recently, Transformers have been applied to image captioning to encode cross-modal information, in conjunction with Convolutional Neural Networks, which provide image region descriptions in terms of embeddings and object labels as input. However, the generated captions sometimes fail to capture the intentions, relationships, and abstract concepts that rely on general or commonsense knowledge. In this work we propose a novel network design, combining the strengths of Transformer models with graph-based models conveying external (common sense) knowledge. Our proposed architecture is a pure vision transformer-based image captioning model, with sequences of image patches used directly as input, without extracting any regional features. In particular, unlike the prior work, our architecture incorporates a knowledge-augmented encoder with a Transformer backbone to inject the external knowledge extracted from a knowledge graph. Furthermore, the bidirectional training on a vision-language corpus of image-text pairs, using modality specific self-supervised learning objectives, achieves promising results compared to the state-of-the-art. Our method has been trained from scratch on a small dataset, achieving a 3.8%, 2.7%, 3.2% and 6.3% improvement in BLEU@4, Meteor, Rouge and Cider scores respectively. We also reported competitive results on the NoCaps dataset, showing that the model generalizes to unseen object categories.