Paper Title
"VISION TO TEXT: ADVANCED IMAGE CAPTIONING WITH TRANSFORMER MODELS"
Article Identifiers
Registration ID: IJNRD_300461
Published ID: IJNRD2409170
: http://doi.one/10.1729/Journal.41517
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Authors
Keywords
Image captioning, InceptionV3 Transformer architecture, Attention Mechanisms, Neural network architectures
Abstract
This project introduces a novel approach to image captioning, leveraging a sophisticated Transformer-based architecture trained on the COCO 2017 dataset. The goal is to smoothly combine natural language processing with computer vision so that a variety of visual content can have evocative captions created for it. Initial steps involve meticulous dataset preprocessing, focusing on a curated subset of 70,000 image-caption pairs. The architecture comprises an InceptionV3-based CNN encoder and Transformer encoder-decoder layers, creating a robust model for image captioning. The training process incorporates a custom loss function and early stopping, resulting in a well-performing model after five epochs. Experimental results demonstrate the model and its ability to generate consistent and contextual captions for different images. Beyond dataset images, the model showcases its versatility by captioning external images provided through URLs. This feature emphasizes the potential real-world applications of the model, beyond the confines of the training dataset.
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How To Cite (APA)
Chinthaparthi Sridhar & Pavani Kotha (September-2024). "VISION TO TEXT: ADVANCED IMAGE CAPTIONING WITH TRANSFORMER MODELS". INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(9), b593-b602. http://doi.one/10.1729/Journal.41517
Issue
Volume 9 Issue 9, September-2024
Pages : b593-b602
Other Publication Details
Paper Reg. ID: IJNRD_300461
Published Paper Id: IJNRD2409170
Research Area: Science and Technology
Author Type: Indian Author
Country: Tirupathi, Andhra Pradhesh, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2409170.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2409170
Crossref DOI: http://doi.one/10.1729/Journal.41517
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