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ABSTRACT
Introduction: In the ever-evolving realm of artificial intelligence, the fusion of visual and auditory cues has become a captivating area of exploration. This abstract delves into the innovative domain of “Image Caption Generation with Audio” employing cutting-edge Deep Learning Techniques.
Background: As visuals and sound often complement each other in our perceptual experience, combining image data with corresponding audio cues presents an opportunity to enhance the context-awareness of automated systems. The synergy between both images and audio opens up possibilities for more nuanced and descriptive captions, enriching the understanding of content.
Objectives: This research aims to leverage deep learning methodologies to create a model that seamlessly integrates visual and auditory information for accurate and contextually rich image captions. By harnessing the power of Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) for sequential audio data, the goal is to enhance the overall captioning accuracy.
Methodology: The proposed model adopts an active learning approach, where the neural network learns to associate audio features with corresponding visual elements. By employing both transfer learning and pre-trained models for audio analysis, the system adapts to diverse datasets, ensuring robust performance across various content types.
Significance: This research has broader implications for applications in accessibility, multimedia content understanding, and human-machine interaction. The ability to generate captions that encompass both visual and auditory dimensions contributes to a more comprehensive AI understanding of the surrounding environment.
Conclusion: Thus As the world of AI continues to evolve, the fusion of image and audio processing through deep learning techniques represents a significant step towards creating more human-like and contextually aware systems.
