mlsanityData to 7 Oct 2026

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

arXiv 2109.05218 ↗First posted 11 Sep 2021cs.CVcited by 5

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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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 17 of its 37 references that are in the map. First posted on arXiv 11 Sep 2021.

Builds on

17 of 37 references.

References · 17

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5 more
  1. 2017Fluency-Guided Cross-Lingual Image Captioningcited by 33
  2. 2020Image Captioning through Image Transformercited by 29
  3. 2018Chittron: An Automatic Bangla Image Captioning Systemcited by 8
  4. 2021Improved Bengali Image Captioning via deep convolutional neural network based encoder-decoder modelcited by 6
  5. 2020TextMage: The Automated Bangla Caption Generator Based On Deep Learningcited by 2

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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 2.

5 papers.