# Image captioning for Brazilian Portuguese using GRIT model

arXiv: https://arxiv.org/abs/2402.05106 · HTML: https://mlsanity.com/paper/2402.05106/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 6 references that are in the map.
- First posted on arXiv 7 Feb 2024.

## Builds on (most cited first, 3 of 3)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [GRIT: Faster and Better Image captioning Transformer Using Dual Visual Features](https://mlsanity.com/paper/2207.09666/) (2022)

## Built on by (most cited first, 1 of 1)

- [Brazilian Portuguese Image Captioning with Transformers: A Study on Cross-Native-Translated Dataset](https://mlsanity.com/paper/2602.00393/) (2026)

## Abstract (arXiv)

This work presents the early development of a model of image captioning for the Brazilian Portuguese language. We used the GRIT (Grid - and Region-based Image captioning Transformer) model to accomplish this work. GRIT is a Transformer-only neural architecture that effectively utilizes two visual features to generate better captions. The GRIT method emerged as a proposal to be a more efficient way to generate image captioning. In this work, we adapt the GRIT model to be trained in a Brazilian Portuguese dataset to have an image captioning method for the Brazilian Portuguese Language.