  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.
