A Deep Learning Model to Generate Image Captions

Shashank Parmar, Raman Tyagi and Prince Kumar Dhankar
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Shashank Parmar: Department of Software Engineering, Delhi Technological University, New Delhi, India
Raman Tyagi: Department of Software Engineering, Delhi Technological University, New Delhi, India
Prince Kumar Dhankar: Department of Software Engineering, Delhi Technological University, New Delhi, India

International Journal of Research and Scientific Innovation, 2023, vol. 10, issue 11, 634-643

Abstract: How computers can automatically describe the substance of photographs using human language is a topic that interests us greatly. We choose to use the most advanced image caption generator currently available to obtain a deeper understanding of this computer vision topic. Show, attend and tell Visually attentive neural image caption generator [12]. Our machine learning-based neural network picture description generator is created in Python using the Pytorch ML framework. In our workflow, we’ve determined ï¬ ve key elements: Data preparation, a convolutional neural network (CNN) it helps in encoding, a recurrent neural network (RNN) it helps in decoding, a beam search to determine the best description, generation of sentences, and assessment make up the R1-R6 framework. The quality and correctness of the generated caption are evaluated using the BLEU-4 score. Each member of our group contributed equally to moving the project forward as we distributed the ï¬ ve elements mentioned above equally among ourselves. All ï¬ ve components have been completed successfully, and we can now use Kaggle Notebook to train our network. After the network has been trained and is performing satisfactorily, we continue to see the attention mechanism.

Date: 2023
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