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Visual storytelling is a task of generating relevant and interesting storiesfor given image sequences. In this work we aim at increasing the diversity ofthe generated stories while preserving the informative content from the images.We propose to foster the diversity and informativeness of a generated story byusing a concept selection module that suggests a set of concept candidates.Then, we utilize a large scale pre-trained model to convert concepts and imagesinto full stories. To enrich the candidate concepts, a commonsense knowledgegraph is created for each image sequence from which the concept candidates areproposed. To obtain appropriate concepts from the graph, we propose two novelmodules that consider the correlation among candidate concepts and theimage-concept correlation. Extensive automatic and human evaluation resultsdemonstrate that our model can produce reasonable concepts. This enables ourmodel to outperform the previous models by a large margin on the diversity andinformativeness of the story, while retaining the relevance of the story to theimage sequence.

Commonsense Knowledge Aware Concept Selection For Diverse and Informative Visual Storytelling