Incremental few-shot learning has emerged as a new and challenging area in
deep learning, whose objective is to train deep learning models using very few
samples of new class data, and none of the old class data. In this work we
tackle the problem of batch incremental few-shot road object detection using
data from the India Driving Dataset (IDD). Our approach, DualFusion, combines
object detectors in a manner that allows us to learn to detect rare objects
with very limited data, all without severely degrading the performance of the
detector on the abundant classes. In the IDD OpenSet incremental few-shot
detection task, we achieve a mAP50 score of 40.0 on the base classes and an
overall mAP50 score of 38.8, both of which are the highest to date. In the COCO
batch incremental few-shot detection task, we achieve a novel AP score of 9.9,
surpassing the state-of-the-art novel class performance on the same by over 6.6
times.

在本文中，我们使用来自印度驾驶数据集的数据，采用 DualFusion 方法解决批量渐进式少样本道路物体检测的问题，利用对象检测器组合，使我们学会检测仅有极为有限数据的稀有对象，而仍不会严重降低检测器在丰富类上的性能。在 IDD OpenSet 逐步少样本检测任务中，我们的 mAP50 分别为 40.0 和 38.8，在 COCO 批量渐进式少样本检测任务中，我们获得了一项新的 AP 得分 9.9，超过同一任务上的现有类最佳表现 6.6 倍。