Tag: representation learning

Abstract: Recent advances in remote sensing have expanded Earth observation through an increasing number of sensing modalities, where each sensor provides distinct spectral, spatial, and temporal information. Effective analysis of these data depends on learning representations that integrate complementary information across modalities while supporting multiple learning objectives. Multi-task multi-modal learning in remote sensing remains challenging […]

Abstract: Triplet loss traditionally relies only on class labels and does not use all available information in multi-task scenarios where multiple types of annotations are available. This paper introduces a Multi-Annotation Triplet Loss (MATL) framework that extends triplet loss by incorporating additional annotations, such as bounding box information, alongside class labels in the loss formulation. […]