A Comparative Evaluation of Transformer-Based Vision Encoder-Decoder Models for Brazilian Portuguese Image Captioning

Image captioning refers to the process of creating a natural language description for one or more images. This task has several practical applications, from aiding in medical diagnoses through image descriptions to promoting social inclusion by providing visual context to people with impairments. Despite recent progress, especially in English, low-resource languages like Brazilian Portuguese face a shortage of datasets, models, and studies. This work seeks to contribute to this context by fine-tuning and investigating the performance of vision language models based on the Transformer architecture in Brazilian Portuguese. We leverage pre-trained vision model checkpoints (ViT, Swin, and DeiT) and neural language models (BERTimbau, DistilBERTimbau, and GPorTuguese-2). Several experiments were carried out to compare the efficiency of different model combinations using the #PraCegoVer-63K, a native Portuguese dataset, and a translated version of the Flickr30K dataset. The experimental results demonstrated that configurations using the Swin, DistilBERTimbau, and GPorTuguese-2 models generally achieved the best outcomes. Furthermore, the #PraCegoVer-63K dataset presents a series of challenges, such as descriptions made up of multiple sentences and the presence of proper names of places and people, which significantly decrease the performance of the investigated models.