# Contextualize, Show and Tell: A Neural Visual Storyteller

arXiv: https://arxiv.org/abs/1806.00738 · HTML: https://mlsanity.com/paper/1806.00738/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2018.

## Built on by (most cited first, 13 of 13)

- [Plot and Rework: Modeling Storylines for Visual Storytelling](https://mlsanity.com/paper/2105.06950/) (2021)
- [VIST-GPT: Ushering in the Era of Visual Storytelling with LLMs?](https://mlsanity.com/paper/2504.19267/) (2025)
- [Ordered Attention for Coherent Visual Storytelling](https://mlsanity.com/paper/2108.02180/) (2021)
- [Detecting and Grounding Important Characters in Visual Stories](https://mlsanity.com/paper/2303.17647/) (2023)
- [Visual Storytelling with Question-Answer Plans](https://mlsanity.com/paper/2310.05295/) (2023)
- [SCO-VIST: Social Interaction Commonsense Knowledge-based Visual Storytelling](https://mlsanity.com/paper/2402.00319/) (2024)
- [ImageChain: Advancing Sequential Image-to-Text Reasoning in Multimodal Large Language Models](https://mlsanity.com/paper/2502.19409/) (2025)
- [Visual Transformation Telling](https://mlsanity.com/paper/2305.01928/) (2023)
- [Text-Only Training for Visual Storytelling](https://mlsanity.com/paper/2308.08881/) (2023)
- [Story Generation from Visual Inputs: Techniques, Related Tasks, and Challenges](https://mlsanity.com/paper/2406.02748/) (2024)
- [Context-aware Visual Storytelling with Visual Prefix Tuning and Contrastive Learning](https://mlsanity.com/paper/2408.06259/) (2024)
- [Generating Visual Stories with Grounded and Coreferent Characters](https://mlsanity.com/paper/2409.13555/) (2024)
- [From Image Captioning to Visual Storytelling](https://mlsanity.com/paper/2508.14045/) (2025)

## Abstract (arXiv)

We present a neural model for generating short stories from image sequences, which extends the image description model by Vinyals et al. (Vinyals et al., 2015). This extension relies on an encoder LSTM to compute a context vector of each story from the image sequence. This context vector is used as the first state of multiple independent decoder LSTMs, each of which generates the portion of the story corresponding to each image in the sequence by taking the image embedding as the first input. Our model showed competitive results with the METEOR metric and human ratings in the internal track of the Visual Storytelling Challenge 2018.