Multi Modal Approach

Multi-modal approaches integrate data from multiple sources (e.g., images, text, audio, sensor readings) to improve the accuracy and robustness of machine learning models. Current research focuses on efficient fusion techniques, often employing transformer architectures or convolutional neural networks, to combine diverse data types and address challenges like variable data lengths and imbalanced modalities. This methodology is proving valuable across diverse fields, enhancing applications ranging from medical diagnosis (e.g., brain tumor classification, stress detection) to robotics (e.g., underwater target localization, material identification) and improving the performance of tasks like emotion recognition and artwork classification.