用多类型标注增强三元组损失，提升目标检测的分类与定位能力
Multi-Task Learning with Multi-Annotation Triplet Loss for Improved Object Detection
- 在三元组损失中融合类别标签和边界框信息
- 在无人机野生动物图像上实现分类与定位双提升
- 适合需要联合学习分类与定位的任务场景
传统三元组损失仅使用类别标签，未充分利用多任务场景中丰富的标注信息。本文提出多标注三元组损失（MATL）框架，在损失函数中引入边界框等额外标注，与类别标签共同优化。通过融合互补信息，MATL显著提升需同时进行分类与定位的任务性能。在无人机野生动物影像数据集上的实验表明，MATL在分类和定位两个指标上均优于传统三元组损失，验证了利用全部可用标注对多任务学习的积极影响。
原文摘要 · Abstract (English)
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.
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