# Bornon: Bengali Image Captioning with Transformer-based Deep learning approach

arXiv: https://arxiv.org/abs/2109.05218 · HTML: https://mlsanity.com/paper/2109.05218/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 37 references that are in the map.
- First posted on arXiv 11 Sep 2021.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Image Captioning with Semantic Attention](https://mlsanity.com/paper/1603.03925/) (2016)
- [SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioning](https://mlsanity.com/paper/1611.05594/) (2016)
- [Image Captioning: Transforming Objects into Words](https://mlsanity.com/paper/1906.05963/) (2019)
- [Convolutional Image Captioning](https://mlsanity.com/paper/1711.09151/) (2017)
- [STAIR Captions: Constructing a Large-Scale Japanese Image Caption Dataset](https://mlsanity.com/paper/1705.00823/) (2017)
- [Fluency-Guided Cross-Lingual Image Captioning](https://mlsanity.com/paper/1708.04390/) (2017)
- [Image Captioning through Image Transformer](https://mlsanity.com/paper/2004.14231/) (2020)
- [Chittron: An Automatic Bangla Image Captioning System](https://mlsanity.com/paper/1809.00339/) (2018)
- [Improved Bengali Image Captioning via deep convolutional neural network based encoder-decoder model](https://mlsanity.com/paper/2102.07192/) (2021)
- [TextMage: The Automated Bangla Caption Generator Based On Deep Learning](https://mlsanity.com/paper/2010.08066/) (2020)

## Built on by (most cited first, 5 of 5)

- [BAN-Cap: A Multi-Purpose English-Bangla Image Descriptions Dataset](https://mlsanity.com/paper/2205.14462/) (2022)
- [Attention-based transformer models for image captioning across languages: An in-depth survey and evaluation](https://mlsanity.com/paper/2506.05399/) (2025)
- [Align Where the Words Look: Cross-Attention-Guided Patch Alignment with Contrastive and Transport Regularization for Bengali Captioning](https://mlsanity.com/paper/2509.18369/) (2025)
- [FaceGemma: Enhancing Image Captioning with Facial Attributes for Portrait Images](https://mlsanity.com/paper/2309.13601/) (2023)
- [Image captioning in different languages](https://mlsanity.com/paper/2407.09495/) (2024)

## Abstract (arXiv)

Image captioning using Encoder-Decoder based approach where CNN is used as the Encoder and sequence generator like RNN as Decoder has proven to be very effective. However, this method has a drawback that is sequence needs to be processed in order. To overcome this drawback some researcher has utilized the Transformer model to generate captions from images using English datasets. However, none of them generated captions in Bengali using the transformer model. As a result, we utilized three different Bengali datasets to generate Bengali captions from images using the Transformer model. Additionally, we compared the performance of the transformer-based model with a visual attention-based Encoder-Decoder approach. Finally, we compared the result of the transformer-based model with other models that employed different Bengali image captioning datasets.