Cross Modal Fusion

Cross-modal fusion aims to integrate information from different data modalities (e.g., images, text, audio) to create richer, more robust representations for various tasks. Current research emphasizes developing efficient and effective fusion strategies, often employing transformer-based architectures and attention mechanisms to capture complex inter-modal relationships, as well as exploring different fusion points (early, mid, late) depending on the task and data characteristics. This field is significant because improved cross-modal understanding has broad applications, enhancing performance in areas such as image segmentation, video understanding, recommendation systems, and emotion recognition.