# Diverse and Coherent Paragraph Generation from Images

arXiv: https://arxiv.org/abs/1809.00681 · HTML: https://mlsanity.com/paper/1809.00681/

## Key facts

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

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

- [ZeroCap: Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic](https://mlsanity.com/paper/2111.14447/) (2021)
- [From Show to Tell: A Survey on Deep Learning-based Image Captioning](https://mlsanity.com/paper/2107.06912/) (2021)
- [Language Models Can See: Plugging Visual Controls in Text Generation](https://mlsanity.com/paper/2205.02655/) (2022)
- [Visual Clues: Bridging Vision and Language Foundations for Image Paragraph Captioning](https://mlsanity.com/paper/2206.01843/) (2022)
- [IC3: Image Captioning by Committee Consensus](https://mlsanity.com/paper/2302.01328/) (2023)
- [CLID: Controlled-Length Image Descriptions with Limited Data](https://mlsanity.com/paper/2211.14835/) (2022)
- [Enhancing image captioning with depth information using a Transformer-based framework](https://mlsanity.com/paper/2308.03767/) (2023)
- [Knowledge driven Description Synthesis for Floor Plan Interpretation](https://mlsanity.com/paper/2103.08298/) (2021)
- [Matching Visual Features to Hierarchical Semantic Topics for Image Paragraph Captioning](https://mlsanity.com/paper/2105.04143/) (2021)
- [RIHA: Report-Image Hierarchical Alignment for Radiology Report Generation](https://mlsanity.com/paper/2604.27559/) (2026)
- [Describing image focused in cognitive and visual details for visually impaired people: An approach to generating inclusive paragraphs](https://mlsanity.com/paper/2202.05331/) (2022)
- [Bypass Network for Semantics Driven Image Paragraph Captioning](https://mlsanity.com/paper/2206.10059/) (2022)
- [A Fine-Grained Image Description Generation Method Based on Joint Objectives](https://mlsanity.com/paper/2311.12799/) (2023)

## Abstract (arXiv)

Paragraph generation from images, which has gained popularity recently, is an important task for video summarization, editing, and support of the disabled. Traditional image captioning methods fall short on this front, since they aren't designed to generate long informative descriptions. Moreover, the vanilla approach of simply concatenating multiple short sentences, possibly synthesized from a classical image captioning system, doesn't embrace the intricacies of paragraphs: coherent sentences, globally consistent structure, and diversity. To address those challenges, we propose to augment paragraph generation techniques with 'coherence vectors', 'global topic vectors', and modeling of the inherent ambiguity of associating paragraphs with images, via a variational auto-encoder formulation. We demonstrate the effectiveness of the developed approach on two datasets, outperforming existing state-of-the-art techniques on both.