Decoding Visual Question Answering Methodologies: Unveiling Applications in Multimodal Learning Frameworks

This research investigates the intricacies of Visual Question Answering (VQA) methodologies and their applications within Multimodal Learning Frameworks. Our approach, founded on the synergy of Multimodal Compact Bilinear Pooling (MCB) and Neural Module Networks (NMN), offers a comprehensive understanding of visual and textual elements. Notably, the model excels in responding to Descriptive questions with an accuracy of 88%, showcasing a nuanced grasp of detailed inquiries. Factual questions follow closely with an 86% accuracy, while Inferential questions exhibit commendable performance at 82%. Precision scores reinforce the model's reliability, registering 85% for Descriptive, 82% for Factual, and 78% for inferential questions. Robust recall scores further emphasize the model's ability to retrieve relevant information across question types. The F1 Score, reflecting a harmonious blend of precision and recall, attests to the model's strong overall performance: 87% for Descriptive, 84% for Factual, and 80% for inferential questions. Visualizations through boxplots and violin plots affirm the model's consistency in accuracy and precision across question types. Future directions encompass dataset expansion, integration of transfer learning, attention mechanisms for interpretability, and exploration of broader multimodal applications beyond VQA. This research establishes a resilient framework for advancing VQA methodologies, paving the way for enhanced multimodal learning in diverse contexts.

@article{Devi2024,
  title     = {Decoding Visual Question Answering Methodologies: Unveiling Applications in Multimodal Learning Frameworks},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {7},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Y Harika Devi and G Ramu},
  doi       = {10.14569/IJACSA.2024.0150773},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150773}
}