Visual Analytics for Efficient Image-to-Text Prediction Based on Visually-Aware Context Learning

Visual Analytics for Efficient Image-to-Text Prediction Based on Visually-Aware Context Learning

Registration ID: IJNRD_220982

Published ID: IJNRD2405425

DOI: Click Here to Get

Naga Venkata Subramanya Nithin Ranga , Nandipati Varshith Naga Sri Pavan , Gummadi Varshitha , S.Revathy

Natural Language Processing, Ground Truth, Recurrent Neural Network, Bidirectional RNN, Region CNN,Convolutional Neural Network

In the realm of image captioning, attention has predominantly been directed towards foreground objects, but a notable shift in focus has emerged, particularly evident in the context of geological images of rocks. Unlike traditional models that employ detection-based attention mechanisms, which often result in inaccurate captions by encompassing irrelevant backgrounds or overlapping regions, our approach seeks to address this challenge by refining attention to finer details. While convolutional neural networks (CNNs) have been a staple for both encoding and decoding in existing models, the crux of accurate image captioning lies in grasping the intricate semantic relationships between diverse objects within an image. Our methodology advances current practices by extracting feature vectors from meticulously segmented regions, enabling a more nuanced understanding of image components. Furthermore, we introduce a dual-attention module designed to independently process features from distinct classes, thereby enhancing the model's ability to discern complex scenes. Through rigorous experimentation, our model demonstrates proficiency in recognizing overlapping objects and comprehending scenes holistically, ultimately yielding competitive performance when benchmarked against state-of-the-art techniques.

Published Paper   E-Certificate

"Visual Analytics for Efficient Image-to-Text Prediction Based on Visually-Aware Context Learning", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 5, page no.e200-e210, May-2024, Available :https://ijnrd.org/papers/IJNRD2405425.pdf

Volume 9 Issue 5, May-2024

Pages : e200-e210

Paper Reg. ID: IJNRD_220982

Published Paper Id: IJNRD2405425

Downloads: 000121154

Research Area: Computer Science & Technology 

Country: Chirala, Andhra Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2405425.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2405425

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

Call For Paper - Volume 10 | Issue 8 | August 2025

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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to explore advances in research pertaining to applied, theoretical and experimental Technological studies. The goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in and around the world. IJNRD will provide an opportunity for practitioners and educators of engineering field to exchange research evidence, models of best practice and innovative ideas.

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