Modal Fusion

Modal fusion integrates information from multiple data sources (modalities), such as images, audio, and text, to improve the performance of machine learning models. Current research focuses on developing effective fusion strategies, including early, late, and intermediate fusion approaches, often employing transformer networks, contrastive learning, and attention mechanisms to align and combine features from different modalities. This field is significant because it enables more robust and accurate analysis in diverse applications, ranging from semantic segmentation and emotion recognition to plant identification and medical diagnosis, where single-modality data may be insufficient or incomplete. Improved modal fusion techniques are driving advancements in various fields by leveraging the complementary strengths of different data types.