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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
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Impact Factor : 8.76
Issue per Year : 12
Volume Published : 9
Issue Published : 96
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Published Paper Details
Paper Title:
From Pixels To Text: Deep Learning Approach For Image Caption Generation
Authors Name:
NARESH SHARMA
, HARI OM
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Author Reg. ID:
IJNRD_213645
Published Paper Id:
IJNRD2403035
Published In:
Volume 9 Issue 3, March-2024
DOI:
Abstract:
In the realm of computer vision and natural language processing, the synthesis of image understanding and language comprehension has been a longstanding challenge. Over the past decade, deep learning methodologies have emerged as powerful tools in addressing this challenge. Several research works have focused on mitigating dataset bias, incorporating vision-language pre-training methods, and developing improved evaluation tools to enhance the quality of image captions. The use of end-to-end models has been highlighted as an impressive aspect, allowing the prediction of image captions without the need for complex data preparation or a pipeline of specifically designed models. While the field of automatic image captioning presents numerous complexities, the combination of deep learning, computer vision, and natural language processing has facilitated significant progress. Ongoing advancements in hardware and deep learning models are expected to further improve the accuracy of caption generation in the future. Through a comprehensive review of existing methodologies and recent innovations, this paper outlines the evolution of image captioning techniques. It discusses the pivotal role played by attention mechanisms in enhancing the quality and relevance of generated captions by focusing on salient regions of the image. Furthermore, this paper sheds light on the challenges encountered in image caption generation, such as ambiguity in interpretation and diversity in visual content. Strategies for mitigating these challenges, including reinforcement learning and adversarial training, are explored to push the boundaries of captioning performance. In conclusion, this paper provides insights into the current state-of-the-art in image caption generation and offers directions for future research. By integrating deep learning techniques with image understanding and language generation, the quest for machines to comprehend and describe visual content continues to evolve, opening new avenues for human-machine interaction and multimedia applications.
Keywords:
Deep Learning, Image Caption Generation, Neural Networks, Computer Vision, Natural Language Processing.
Cite Article:
"From Pixels To Text: Deep Learning Approach For Image Caption Generation" , International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 3, page no.a315-a327, March-2024, Available :http://www.ijnrd.org/papers/IJNRD2403035.pdf
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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
Publication Details:
Published Paper ID:IJNRD2403035
Registration ID: 213645
Published In: Volume 9 Issue 3, March-2024
DOI (Digital Object Identifier):
Page No: a315-a327
Country: SATHYAMANGLAM, TAMIL NADU, India
Research Area: Computer Science & Technology
Publisher : IJ Publication
Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2403035
Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2403035
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016
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