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Interactive machine learning (IML) is a beneficial learning paradigm in casesof limited data availability, as human feedback is incrementally integratedinto the training process. In this paper, we present an IML pipeline for imagecaptioning which allows us to incrementally adapt a pre-trained imagecaptioning model to a new data distribution based on user input. In order toincorporate user input into the model, we explore the use of a combination ofsimple data augmentation methods to obtain larger data batches for each newlyannotated data instance and implement continual learning methods to preventcatastrophic forgetting from repeated updates. For our experiments, we split adomain-specific image captioning dataset, namely VizWiz, into non-overlappingparts to simulate an incremental input flow for continually adapting the modelto new data. We find that, while data augmentation worsens results, even whenrelatively small amounts of data are available, episodic memory is an effectivestrategy to retain knowledge from previously seen clusters.

Towards Adaptable and Interactive Image Captioning with Data Augmentation and Episodic Memory