Paper Title
Visual Analytics for Efficient Image-to-Text Prediction Based on Visually-Aware Context Learning
Article Identifiers
: Authors
Keywords
Natural Language Processing, Ground Truth, Recurrent Neural Network, Bidirectional RNN, Region CNN,Convolutional Neural Network
Abstract
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.
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How To Cite (APA)
Naga Venkata Subramanya Nithin Ranga, Nandipati Varshith Naga Sri Pavan, Gummadi Varshitha, & S.Revathy (May-2024). Visual Analytics for Efficient Image-to-Text Prediction Based on Visually-Aware Context Learning. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(5), e200-e210. https://ijnrd.org/papers/IJNRD2405425.pdf
Issue
Volume 9 Issue 5, May-2024
Pages : e200-e210
Other Publication Details
Paper Reg. ID: IJNRD_220982
Published Paper Id: IJNRD2405425
Downloads: 000122001
Research Area: Computer Science & TechnologyÂ
Author Type: Indian Author
Country: Chirala, Andhra Pradesh, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2405425.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2405425
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