  Transfer learning based approaches have recently achieved promising results
on the few-shot detection task. These approaches however suffer from
``catastrophic forgetting'' issue due to finetuning of base detector, leading
to sub-optimal performance on the base classes. Furthermore, the slow
convergence rate of stochastic gradient descent (SGD) results in high latency
and consequently restricts real-time applications. We tackle the aforementioned
issues in this work. We pose few-shot detection as a hierarchical learning
problem, where the novel classes are treated as the child classes of existing
base classes and the background class. The detection heads for the novel
classes are then trained using a specialized optimization strategy, leading to
significantly lower training times compared to SGD. Our approach obtains
competitive novel class performance on few-shot MS-COCO benchmark, while
completely retaining the performance of the initial model on the base classes.
We further demonstrate the application of our approach to a new class-refined
few-shot detection task.
