Advancing Multimodal Learning for Robust Pattern Recognition

Description

Page: 15-20

Namrata Ravindra Jagtap¹, Deepali Akolkar², and Rushikesh Ravindra Jagtap³ (Department of Statistics, Dr. D.Y. Patil Arts Commerce and Science, Pimpri, Pune, Maharashtra^1,2 and Department of Computer Science, Indira College of Commerce and Science, Tathawade, Pune, Maharashtra³)

Traditional pattern recognition models often struggle to generalize effectively across different domains, limiting their real-world applicability in fields such as image recognition and medical imaging. This study explores advanced deep learning techniques, including domain adaptation strategies, feature transformation methods, and multimodal fusion architectures, to enhance cross-domain recognition capabilities. To evaluate these approaches, we conduct extensive experiments on benchmark datasets, analyzing key performance metrics such as accuracy, F1-score, and inference time. Our findings indicate that multimodal learning significantly improves recognition performance, leading to more robust and adaptable models. Furthermore, we investigate practical challenges associated with real-time deployment, including computational efficiency, latency issues, and model interpretability, and propose solutions to enhance deployment efficiency. The insights from this research contribute to the development of scalable and flexible pattern recognition systems that generalize effectively beyond their training domains.