Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12188/23134
Title: | Stories for images-in-sequence by using visual and narrative components | Authors: | Smilevski, Marko Lalkovski, Ilija Madjarov, Gjorgji |
Keywords: | Visual storytelling · Deep learning · Vision-to-language | Issue Date: | 17-Sep-2018 | Publisher: | Springer, Cham | Conference: | International Conference on Telecommunications | 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 imagesin-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. | URI: | http://hdl.handle.net/20.500.12188/23134 |
Appears in Collections: | Faculty of Computer Science and Engineering: Conference papers |
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1805.05622.pdf | 2.76 MB | Adobe PDF | View/Open |
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