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

arXiv: https://arxiv.org/abs/1805.05622 · HTML: https://mlsanity.com/paper/1805.05622/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2018.

## Built on by (most cited first, 2 of 2)

- [SCO-VIST: Social Interaction Commonsense Knowledge-based Visual Storytelling](https://mlsanity.com/paper/2402.00319/) (2024)
- [Latent Beam Diffusion Models for Generating Visual Sequences](https://mlsanity.com/paper/2503.20429/) (2025)

## Abstract (arXiv)

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.