Synthesis of Vision and Language: Multifaceted Image Captioning Application

Synthesis of Vision and Language: Multifaceted Image Captioning Application

 Arpit Gupta, Himanshu Goyal, Ishita Kohli

 Department of IT, Maharaja Agrasen Institute of Technology, India

Abstract— The rapid advancement in image captioning has been a pivotal area of research, aiming to mimic human-like understanding of visual content. This paper presents an innovative approach that integrates attention mechanisms and object features into an image captioning model. Leveraging the Flickr8k dataset, this research explores the fusion of these components to enhance image comprehension and caption generation. Furthermore, the study s howcases the implementation of this model in a user-friendly application using FASTAPI and ReactJS, offering text-to-speech translation in multiple languages. The findings underscore the efficacy of this approach in advancing image captioning technology. This tutorial outlines the construction of an image caption generator, employing Convolutional Neural Network (CNN) for image feature extraction and Long Short-Term Memory Network (LSTM) for Natural Language Processing (NLP).

Keywords—Convolutional Neural Networks, Long Short Term Memory, Attention Mechanism, Transformer Architecture, Vision Transformers, Transfer Learning, Multimodal fusion, Deep Learning Models, Pre-Trained Models, Image Processing Techniques