Cross Modality Fusion

Cross-modality fusion aims to integrate information from different data sources (e.g., images, text, audio) to improve the performance of machine learning models beyond what's achievable with single modalities. Current research focuses on developing effective fusion strategies, often employing transformer-based architectures, attention mechanisms, and contrastive learning to align and combine features from diverse modalities. This field is significant because improved cross-modality fusion leads to more robust and accurate systems in various applications, including object detection, image retrieval, emotion recognition, and healthcare diagnostics.