Modality Dependency

Modality dependency research focuses on effectively integrating information from multiple data sources (modalities) to improve the accuracy and robustness of machine learning models. Current efforts concentrate on developing sophisticated architectures, such as variational autoencoders and graph-based models, to capture both the relationships within individual modalities (intra-modal dependencies) and the interactions between them (inter-modal dependencies). This work is crucial for advancing applications in diverse fields like healthcare (e.g., brain tumor segmentation), emotion recognition, and multimodal data analysis, where leveraging the combined power of different data types is essential for achieving superior performance.