Cross Modal Interaction

Cross-modal interaction research focuses on effectively integrating information from different data modalities (e.g., text, images, audio) to improve the performance of AI systems. Current research emphasizes developing novel architectures, such as multimodal transformers and graph neural networks, and innovative training paradigms like cross-modal denoising and alternating unimodal adaptation, to achieve better cross-modal alignment and feature fusion. This field is significant because improved cross-modal understanding is crucial for advancing applications in diverse areas, including image segmentation, robotics, and medical diagnosis, by enabling AI systems to process and interpret richer, more nuanced information.