Few-Shot Personality-Specific Image Captioning via Meta-Learning

In standard captioning, the characteristics of the end-user whom we generate the caption for are ignored. This is mainly because more than often we do not have access to the entire spectrum of personality characteristics for our user. In other words, each user in test time can exhibit different traits to which we need to adapt our model. Therefore, we focus on generating personalized image captioning and formulate the problem as a few-shot learning setting. To the best of our knowledge, we are the first to study this problem and shed light on the challenges involved with this setting. Furthermore, we propose a MAML-based few-shot learner enabling the model to learn a new personality style from only a handful of annotated samples. Finally, we set up baselines for the problem and show that our proposed method is superior in performance when compared with baselines on the benchmark dataset. Ablation studies are conducted to investigate different design choices' effects on the model performance.