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arXiv · 1805.05622

Stories for Images-in-Sequence by using Visual and Narrative Components

Marko Smilevski·Ilija Lalkovski·Gjorgji Madjarov
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Abstract

Recent research in AI is focusing towards generating narrative stories about visual scenes. It has the potential to achieve more human-like understanding than just basic description generation of images- in-sequence. In this work, we propose a solution for generating stories for images-in-sequence that is based on the Sequence to Sequence model. As a novelty, our encoder model is composed of two separate encoders, one that models the behaviour of the image sequence and other that models the sentence-story generated for the previous image in the sequence of images. By using the image sequence encoder we capture the temporal dependencies between the image sequence and the sentence-story and by using the previous sentence-story encoder we achieve a better story flow. Our solution generates long human-like stories that not only describe the visual context of the image sequence but also contains narrative and evaluative language. The obtained results were confirmed by manual human evaluation.

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Marko Smilevski, Ilija Lalkovski, Gjorgji Madjarov. 2018-05-15. Stories for Images-in-Sequence by using Visual and Narrative Components. https://doi.org/10.1007/978-3-030-00825-3_13

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