# Describing image focused in cognitive and visual details for visually impaired people: An approach to generating inclusive paragraphs

arXiv: https://arxiv.org/abs/2202.05331 · HTML: https://mlsanity.com/paper/2202.05331/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 33 references that are in the map.
- First posted on arXiv 10 Feb 2022.

## Builds on (most cited first, 19 of 19)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Show and Tell: A Neural Image Caption Generator](https://mlsanity.com/paper/1411.4555/) (2014)
- [Challenges in Representation Learning: A report on three machine learning contests](https://mlsanity.com/paper/1307.0414/) (2013)
- [Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning](https://mlsanity.com/paper/1612.01887/) (2016)
- [DenseCap: Fully Convolutional Localization Networks for Dense Captioning](https://mlsanity.com/paper/1511.07571/) (2015)
- [Image Captioning: Transforming Objects into Words](https://mlsanity.com/paper/1906.05963/) (2019)
- [Captioning Images Taken by People Who Are Blind](https://mlsanity.com/paper/2002.08565/) (2020)
- [A Hierarchical Approach for Generating Descriptive Image Paragraphs](https://mlsanity.com/paper/1611.06607/) (2016)
- [CPTR: Full Transformer Network for Image Captioning](https://mlsanity.com/paper/2101.10804/) (2021)
- [Dense Captioning with Joint Inference and Visual Context](https://mlsanity.com/paper/1611.06949/) (2016)
- [Context and Attribute Grounded Dense Captioning](https://mlsanity.com/paper/1904.01410/) (2019)
- [Recurrent Topic-Transition GAN for Visual Paragraph Generation](https://mlsanity.com/paper/1703.07022/) (2017)
- [Understanding Guided Image Captioning Performance across Domains](https://mlsanity.com/paper/2012.02339/) (2020)
- [Image Captioning as an Assistive Technology: Lessons Learned from VizWiz 2020 Challenge](https://mlsanity.com/paper/2012.11696/) (2020)
- [Diverse and Coherent Paragraph Generation from Images](https://mlsanity.com/paper/1809.00681/) (2018)

## Abstract (arXiv)

Several services for people with visual disabilities have emerged recently due to achievements in Assistive Technologies and Artificial Intelligence areas. Despite the growth in assistive systems availability, there is a lack of services that support specific tasks, such as understanding the image context presented in online content, e.g., webinars. Image captioning techniques and their variants are limited as Assistive Technologies as they do not match the needs of visually impaired people when generating specific descriptions. We propose an approach for generating context of webinar images combining a dense captioning technique with a set of filters, to fit the captions in our domain, and a language model for the abstractive summary task. The results demonstrated that we can produce descriptions with higher interpretability and focused on the relevant information for that group of people by combining image analysis methods and neural language models.