# Multi-Task Learning with Multi-Annotation Triplet Loss for Improved Object Detection

arXiv: https://arxiv.org/abs/2504.08054 · HTML: https://mlsanity.com/paper/2504.08054/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 11 references that are in the map.
- First posted on arXiv 10 Apr 2025.

## Builds on (most cited first, 4 of 4)

- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [Mitigating Negative Transfer in Multi-Task Learning with Exponential Moving Average Loss Weighting Strategies](https://mlsanity.com/paper/2211.12999/) (2022)
- [Shared Manifold Learning Using a Triplet Network for Multiple Sensor Translation and Fusion with Missing Data](https://mlsanity.com/paper/2210.17311/) (2022)

## Built on by (most cited first, 2 of 2)

- [Task-Guided Multi-Annotation Triplet Learning for Remote Sensing Representations](https://mlsanity.com/paper/2604.03837/) (2026)
- [Beyond Task-Driven Features for Object Detection](https://mlsanity.com/paper/2604.03839/) (2026)

## Abstract (arXiv)

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.