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过去的工作大都聚焦在小类样本类别性能而牺牲了大类样本的性能。本文提出一种无遗忘效应的小类样本目标检测器，能够在实现更好的小类样本类别性能的同时，不掉落大类样本类别的性能。在本文中，我们发现了预训练的检测器很少在未见过的类别上产生假阳性预测，且还发现RPN并非理想的类别无关组件。基于这两点发现，我们设计了Re-detector和Bias-Balanced RPN两个简单而有效的结构，只增加少量参数和推断时间即可实现无遗忘效应的小类样本目标检测。
Generalized Few-Shot Object Detection without Forgetting
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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.
