Modality Unifying Network

Modality unifying networks aim to effectively integrate information from multiple data sources (modalities), such as images, LiDAR, and inertial measurements, to improve the accuracy and robustness of various tasks. Current research focuses on developing architectures that handle the inherent differences between modalities, often employing asymmetric fusion strategies or disentangling modality-specific and shared features. These techniques are proving valuable in diverse applications, including gait recognition, unsupervised domain adaptation, and medical image analysis, where combining information from different imaging modalities enhances diagnostic capabilities and improves model performance.