Md Abidur Rahman Khan Jim

Ahsanullah University of Science and Technology

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Shimul Paul

Ahsanullah University of Science and Technology

Mayeesha Humaira

Ahsanullah University of Science and Technology

Faisal Muhammad Shah

Ahsanullah University of Science and Technology

Amit Saha Ami

Ahsanullah University of Science and Technology

Sajib Kumar Saha Joy

Ahsanullah University of Science and Technology

Tonmoy Hossain

University of Virginia

Farzad Ahmed

Ahsanullah University of Science and Technology

Sifat Ahmed

Ahsanullah University of Science and Technology

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Ahsanullah University of Science and Technology

University of Science and Technology

University of Dhaka

University of Virginia

Bangladesh University of Engineering and Technology

American University of Science and Technology

Bangladesh Atomic Energy Commission

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

arXiv (Cornell University) (2021)

Faisal Muhammad Shah Mayeesha Humaira Md Abidur Rahman Khan Jim Amit Saha Ami Shimul Paul

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.

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10.48550/arxiv.2109.05218

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Bengali Image Captioning with Visual Attention

Amit Saha Ami Mayeesha Humaira Md Abidur Rahman Khan Jim Shimul Paul Faisal Muhammad Shah

Attention based approaches has been manifested to be an effective method in image captioning. However, attention can be used on text called semantic attention or on image which in known as spatial attention. We chose to implement the later as the main problem of image captioning is not being able to detect objects in image properly. In this work, we develop an approach which extracts features from images using two different convolutional neural network and combines the features with an attention model in order to generate caption with an RNN. We adapted Xception and InceptionV3 as our CNN and GRU as our RNN. Moreover, we Evaluated our proposed model on Flickr8k dataset translated into Bengali. So that captions can be generated in Bengali using visual attention.

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10.1109/iccit51783.2020.9392709

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Bornon: Bengali Image Captioning with Transformer-Based Deep Learning Approach

SN Computer Science (2021)

Faisal Muhammad Shah Mayeesha Humaira Md Abidur Rahman Khan Jim Amit Saha Ami Shimul Paul

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10.1007/s42979-021-00975-0

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Citations (18)

A Comprehensive Survey of COVID-19 Detection Using Medical Images