Real-world object detection is highly desired to be equipped with the learning expandability that can enlarge its detection classes incrementally. Moreover, such learning from only few annotated training samples further adds the flexibility for the object detector, which is highly expected in many applications such as autonomous driving, robotics, etc. However, such sequential learning scenario with few-shot training samples generally causes catastrophic forgetting and dramatic overfitting. In this paper, to address the above incremental few-shot learning issues, a novel Incremental Few-Shot Object Detection (iFSOD) method is proposed to enable the effective continual learning from few-shot samples. Specifically, a Double-Branch Framework (DBF) is proposed to decouple the feature representation of base and novel (few-shot) class, which facilitates both the old-knowledge retention and new-class adaption simultaneously. Furthermore, a progressive model updating rule is carried out to preserve the long-term memory on old classes effectively when adapt to sequential new classes. Moreover, an inter-task class separation loss is proposed to extend the decision region of new-coming classes for better feature discrimination. We conduct experiments on both Pascal VOC and MS-COCO, which demonstrate that our method can effectively solve the problem of incremental few-shot detection and significantly improve the detection accuracy on both base and novel classes.
翻译:此外,从少数附加说明的培训样本中进行的这种学习进一步增加了物体探测器的灵活性,这在自主驾驶、机器人等许多应用中都是非常预期的。然而,这种连续学习情景,如少发培训样本通常会导致灾难性的遗忘和急剧的过度适应。在本文件中,为了解决上述增量的少见的学习问题,提出了一个新的增量小点物体探测(iFSOD)方法,以便能够从少发样本中有效地持续学习。具体地说,建议采用一个双层框架(DBF),将基础和新型(few-shot)级的特征表示脱钩,这在自主驾驶、机器人等许多应用中是高度预期的。然而,这种有少发培训样本的连续学习情景通常会导致灾难性的遗忘和急剧的过度适应。此外,为了有效保存旧类的长期记忆,本文件还提出了一种渐进式更新规则。此外,提出了一种任务间分离方法,以扩大新到的班级的决定区,以便更好的特征歧视。我们在Pascal VOC和MSCO级同时进行实验,从而有效地改进我们测算基础和新级的精度。