Multi-Task Learning with Multi-Annotation Triplet Loss for Improved Object Detection
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. By using these complementary annotations, MATL improves multi-task learning for tasks requiring both classification and localization. Experiments on an aerial wildlife imagery dataset demonstrate that MATL outperforms conventional triplet loss in both classification and localization. These findings highlight the benefit of using all available annotations for triplet loss in multi-task learning frameworks.
Cite
@article{arxiv.2504.08054,
title = {Multi-Task Learning with Multi-Annotation Triplet Loss for Improved Object Detection},
author = {Meilun Zhou and Aditya Dutt and Alina Zare},
journal= {arXiv preprint arXiv:2504.08054},
year = {2026}
}
Comments
Accepted for Oral Presentation at the 45th IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2025, Brisbane, Australia. 4 pages and 4 figures