Tag: triplet loss

Congratulations to Dr. Meilun Zhou for successfully passing his PhD dissertation exam! As a member of the Machine Learning and Sensing Lab, his research focused on developing advanced methods for multi-task, multi-modal representation learning in remote sensing. His work introduces an annotation-driven framework that creates more informative and aligned representations across diverse sensing modalities, helping […]

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. […]