Modality Effect

The modality effect investigates how different sensory inputs (e.g., audio, visual, text) influence the performance of multimodal machine learning systems. Current research focuses on understanding which modalities are most impactful for specific tasks (like speech recognition or disease prediction), optimizing the fusion of these modalities within deep learning architectures, and disentangling the individual contributions of each modality to improve model accuracy and explainability. This research is crucial for advancing various applications, from improving the robustness of speech recognition in noisy environments to enhancing the accuracy and interpretability of medical diagnoses.