IJET Best Journal Authors & Affiliations

• Koteswara Rao Velpula, Assistant Professor, Department of CSE, Vasireddy Venkatadri Institute of Technology, Nambur, Guntur, Andhra Pradesh, Email: koteswararao@vvit.net
• Mohan Kalyan Guntupalli, UG Student, Department of CSE, Vasireddy Venkatadri Institute of Technology, Nambur, Guntur, Andhra Pradesh, Email: 20BQ1A0567@vvit.net
• Venkatesh Katuri, UG Student, Department of CSE, Vasireddy Venkatadri Institute of Technology, Nambur, Guntur, Andhra Pradesh, Email: 20BQ1A0598@vvit.net
• Saidaiah Kandrakunta, UG Student, Department of CSE, Vasireddy Venkatadri Institute of Technology, Nambur, Guntur, Andhra Pradesh, Email: 20BQ1A0589@vvit.net

Abstract – IJET Best Journal

Visual captioning involves creating descriptions of what is happening in an image. It helps build descriptions that explain the content of images. This paper introduces an innovative approach to caption generation using deep learning, specifically utilizing Convolutional Neural Networks (CNNs) for extracting image features and Long Short-Term Memory (LSTM) networks for generating sequences. Additionally, we incorporate nucleus sampling, a probabilistic technique, to improve the diversity and quality of the generated captions, offering more insightful and contextually relevant descriptions for images. This paper marks a significant advancement in automatic image captioning, showcasing the effectiveness of deep learning techniques combined with sophisticated sampling strategies to produce compelling and informative image descriptions.

Keywords – IJET Best Journal

Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Nucleus Sampling, Natural Language Processing (NLP), Feature Extraction, Deep Learning, IJET Best Journal

References – IJET Best Journal

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