# #PraCegoVer: A Large Dataset for Image Captioning in Portuguese

arXiv: https://arxiv.org/abs/2103.11474 · HTML: https://mlsanity.com/paper/2103.11474/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 47 references that are in the map.
- First posted on arXiv 21 Mar 2021.

## Builds on (most cited first, 18 of 18)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (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)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Microsoft COCO Captions: Data Collection and Evaluation Server](https://mlsanity.com/paper/1504.00325/) (2015)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models](https://mlsanity.com/paper/1505.04870/) (2015)
- [Self-critical Sequence Training for Image Captioning](https://mlsanity.com/paper/1612.00563/) (2016)
- [nocaps: novel object captioning at scale](https://mlsanity.com/paper/1812.08658/) (2018)
- [TextCaps: a Dataset for Image Captioning with Reading Comprehension](https://mlsanity.com/paper/2003.12462/) (2020)
- [Attention on Attention for Image Captioning](https://mlsanity.com/paper/1908.06954/) (2019)
- [Captioning Images Taken by People Who Are Blind](https://mlsanity.com/paper/2002.08565/) (2020)
- [Deep Multimodal Semantic Embeddings for Speech and Images](https://mlsanity.com/paper/1511.03690/) (2015)
- [Attend to You: Personalized Image Captioning with Context Sequence Memory Networks](https://mlsanity.com/paper/1704.06485/) (2017)
- [Quality Estimation for Image Captions Based on Large-scale Human Evaluations](https://mlsanity.com/paper/1909.03396/) (2019)
- [UIT-ViIC: A Dataset for the First Evaluation on Vietnamese Image Captioning](https://mlsanity.com/paper/2002.00175/) (2020)

## Built on by (most cited first, 8 of 8)

- [CIDEr-R: Robust Consensus-based Image Description Evaluation](https://mlsanity.com/paper/2109.13701/) (2021)
- [CAPIVARA: Cost-Efficient Approach for Improving Multilingual CLIP Performance on Low-Resource Languages](https://mlsanity.com/paper/2310.13683/) (2023)
- [Neglected Risks: The Disturbing Reality of Children's Images in Datasets and the Urgent Call for Accountability](https://mlsanity.com/paper/2504.14446/) (2025)
- [KTVIC: A Vietnamese Image Captioning Dataset on the Life Domain](https://mlsanity.com/paper/2401.08100/) (2024)
- [Image captioning in different languages](https://mlsanity.com/paper/2407.09495/) (2024)
- [Brazilian Portuguese Image Captioning with Transformers: A Study on Cross-Native-Translated Dataset](https://mlsanity.com/paper/2602.00393/) (2026)
- [Toward a More Ethical Facial Age Estimation: A Generalized Zero-Shot Benchmark Without Training on Children's Data](https://mlsanity.com/paper/2605.29230/) (2026)
- [Language Models for Portuguese: A Systematic Mapping Study](https://mlsanity.com/paper/2608.18138/) (2026)

## Abstract (arXiv)

Automatically describing images using natural sentences is an important task to support visually impaired people's inclusion onto the Internet. It is still a big challenge that requires understanding the relation of the objects present in the image and their attributes and actions they are involved in. Then, visual interpretation methods are needed, but linguistic models are also necessary to verbally describe the semantic relations. This problem is known as Image Captioning. Although many datasets were proposed in the literature, the majority contains only English captions, whereas datasets with captions described in other languages are scarce. Recently, a movement called PraCegoVer arose on the Internet, stimulating users from social media to publish images, tag #PraCegoVer and add a short description of their content. Thus, inspired by this movement, we have proposed the #PraCegoVer, a multi-modal dataset with Portuguese captions based on posts from Instagram. It is the first large dataset for image captioning in Portuguese with freely annotated images. Further, the captions in our dataset bring additional challenges to the problem: first, in contrast to popular datasets such as MS COCO Captions, #PraCegoVer has only one reference to each image; also, both mean and variance of our reference sentence length are significantly greater than those in the MS COCO Captions. These two characteristics contribute to making our dataset interesting due to the linguistic aspect and the challenges that it introduces to the image captioning problem. We publicly-share the dataset at https://github.com/gabrielsantosrv/PraCegoVer.