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Generating a short story out of an image is arduous. Unlike image captioning,story generation from an image poses multiple challenges: preserving the storycoherence, appropriately assessing the quality of the story, steering thegenerated story into a certain style, and addressing the scarcity ofimage-story pair reference datasets limiting supervision during training. Inthis work, we introduce Plug-and-Play Story Teller (PPST) and improveimage-to-story generation by: 1) alleviating the data scarcity problem byincorporating large pre-trained models, namely CLIP and GPT-2, to facilitate afluent image-to-text generation with minimal supervision, and 2) enabling amore style-relevant generation by incorporating stylistic adapters to controlthe story generation. We conduct image-to-story generation experiments withnon-styled, romance-styled, and action-styled PPST approaches and compare ourgenerated stories with those of previous work over three aspects, i.e., storycoherence, image-story relevance, and style fitness, using both automatic andhuman evaluation. The results show that PPST improves story coherence and hasbetter image-story relevance, but has yet to be adequately stylistic.

Every picture tells a story: Image-grounded controllable stylistic story generation