Stories for Images-in-Sequence by using Visual and Narrative Components
This work addresses the challenge of generating coherent and evaluative stories from image sequences, which could enhance AI's human-like understanding in applications like automated storytelling or content creation, though it appears incremental in method.
The authors tackled the problem of generating narrative stories for sequences of images by proposing a dual-encoder sequence-to-sequence model that captures temporal dependencies and improves story flow. Their solution produced long, human-like stories with narrative language, as confirmed by manual human evaluation.
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.