# GLAC Net: GLocal Attention Cascading Networks for Multi-image Cued Story Generation

arXiv: https://arxiv.org/abs/1805.10973 · HTML: https://mlsanity.com/paper/1805.10973/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2018.

## Built on by (most cited first, 23 of 23)

- [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)
- [GROOViST: A Metric for Grounding Objects in Visual Storytelling](https://mlsanity.com/paper/2310.17770/) (2023)
- [RoViST:Learning Robust Metrics for Visual Storytelling](https://mlsanity.com/paper/2205.03774/) (2022)
- [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)
- [Not (yet) the whole story: Evaluating Visual Storytelling Requires More than Measuring Coherence, Grounding, and Repetition](https://mlsanity.com/paper/2407.04559/) (2024)
- [Let Storytelling Tell Vivid Stories: An Expressive and Fluent Multimodal Storyteller](https://mlsanity.com/paper/2403.07301/) (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)
- [Story Generation from Visual Inputs: Techniques, Related Tasks, and Challenges](https://mlsanity.com/paper/2406.02748/) (2024)
- [Vision Transformer Based Model for Describing a Set of Images as a Story](https://mlsanity.com/paper/2210.02762/) (2022)
- [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)
- [Natural Language Generation from Visual Events: State-of-the-Art and Key Open Questions](https://mlsanity.com/paper/2502.13034/) (2025)
- [A System for Image Understanding using Sensemaking and Narrative](https://mlsanity.com/paper/2201.09880/) (2022)
- [Let's Talk! Striking Up Conversations via Conversational Visual Question Generation](https://mlsanity.com/paper/2205.09327/) (2022)
- [Learning to Write with Coherence From Negative Examples](https://mlsanity.com/paper/2209.10922/) (2022)
- [DiffuVST: Narrating Fictional Scenes with Global-History-Guided Denoising Models](https://mlsanity.com/paper/2312.07066/) (2023)
- [TARN-VIST: Topic Aware Reinforcement Network for Visual Storytelling](https://mlsanity.com/paper/2403.11550/) (2024)
- [From Image Captioning to Visual Storytelling](https://mlsanity.com/paper/2508.14045/) (2025)

## Abstract (arXiv)

The task of multi-image cued story generation, such as visual storytelling dataset (VIST) challenge, is to compose multiple coherent sentences from a given sequence of images. The main difficulty is how to generate image-specific sentences within the context of overall images. Here we propose a deep learning network model, GLAC Net, that generates visual stories by combining global-local (glocal) attention and context cascading mechanisms. The model incorporates two levels of attention, i.e., overall encoding level and image feature level, to construct image-dependent sentences. While standard attention configuration needs a large number of parameters, the GLAC Net implements them in a very simple way via hard connections from the outputs of encoders or image features onto the sentence generators. The coherency of the generated story is further improved by conveying (cascading) the information of the previous sentence to the next sentence serially. We evaluate the performance of the GLAC Net on the visual storytelling dataset (VIST) and achieve very competitive results compared to the state-of-the-art techniques. Our code and pre-trained models are available here.