Oboyob: A sequential-semantic Bengali image captioning engine

Deb, Tonmoay; Ali, Mohammad Zariff Ahsham; Bhowmik, Sanchita; Firoze, Adnan; Ahmed, Syed Shahir; Tahmeed, Muhammad Abeer; Rahman, Nashrah; Rahman, Rashedur M.

doi:10.3233/jifs-179351

Cited by 12 publications

(20 citation statements)

References 21 publications

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“…Moreover, P. Blandfort et al [32] systematically characterize diverse image captions that appear "in the wild" in order to understand how people caption images naturally. Alongside English researchers also generated captions in Chinese [15], [16], Japanese [1], Arabic [12], Bahasa Indonesia [13], Hindi [26] German [29] and Bengali [5], [23]. M. Rahman et al [23] generated image caption in Bengali for the first time followed by T. Deb et al [5].…”

Section: Related Workmentioning

confidence: 99%

“…Researchers of paper [23] used VGG-16 to extract image features and stacked LSTMs. On the contrary, researchers of paper [5] generated image caption using InceptionResnetV2 or VGG-16 and LSTM. They utilized 4000 images of the Flickr8k dataset to generate captions.…”

Section: Related Workmentioning

confidence: 99%

“…They utilized 4000 images of the Flickr8k dataset to generate captions. We modified the merge model adapted by paper [5] to get much better and fluent captions in Bengali.…”

Section: Related Workmentioning

confidence: 99%

“…Only three works have been done on image captioning in Bengali till now. In [23], author's first paper, was where in image captioning in Bengali followed by [5] and [37]. Rahman et al [23] have aimed to outline an automatic image captioning system in Bengali called 'Chittron'.…”

Section: Related Workmentioning

confidence: 99%

“…Research done on Image captioning using other languages [13], [15], [16] is still limited. Few works until now have been conducted on image captioning in Bengali [5], [23], [37] so we aim to explore image captioning in the Bengali language further.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Hybridized Deep Learning Method for Bengali Image Captioning

Humaira¹,

Paul²,

Abidur³

et al. 2021

IJACSA

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An omnipresent challenging research topic in computer vision is the generation of captions from an input image. Previously, numerous experiments have been conducted on image captioning in English but the generation of the caption from the image in Bengali is still sparse and in need of more refining. Only a few papers till now have worked on image captioning in Bengali. Hence, we proffer a standard strategy for Bengali image caption generation on two different sizes of the Flickr8k dataset and BanglaLekha dataset which is the only publicly available Bengali dataset for image captioning. Afterward, the Bengali captions of our model were compared with Bengali captions generated by other researchers using different architectures. Additionally, we employed a hybrid approach based on InceptionResnetV2 or Xception as Convolution Neural Network and Bidirectional Long Short-Term Memory or Bidirectional Gated Recurrent Unit on two Bengali datasets. Furthermore, a different combination of word embedding was also adapted. Lastly, the performance was evaluated using Bilingual Evaluation Understudy and proved that the proposed model indeed performed better for the Bengali dataset consisting of 4000 images and the BanglaLekha dataset.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…They utilized 4000 images of the Flickr8k dataset to generate captions. We modified the merge model adapted by paper [5] to get much better and fluent captions in Bengali.…”

Section: Related Workmentioning

confidence: 99%