Tag: metric learning

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. […]

Congratulations to our labmate Xiaolei Guo for successfully defending her dissertation! Defending a dissertation is the last milestone to completing a Ph.D. at the University of Florida. Xiaolei presented a deep interactive segmentation framework to address the time-consuming task of fine-scale pixel-level image annotation. Utilizing transfer learning, annotators are able to interactively fine-tune a pre-trained […]

Congratulations to our labmate, Xiaolei Guo, for passing her Oral Qualifying Exam and becoming a PhD candidate!  For the remainder of her PhD work, Xiaolei plans to investigate fundamental research questions on “Interactive Segmentation with Deep Metric Learning”. We are excited to see what comes from her work! Great job, Xiaolei!