  Learning to detect an object in an image from very few training examples -
few-shot object detection - is challenging, because the classifier that sees
proposal boxes has very little training data. A particularly challenging
training regime occurs when there are one or two training examples. In this
case, if the region proposal network (RPN) misses even one high
intersection-over-union (IOU) training box, the classifier's model of how
object appearance varies can be severely impacted. We use multiple distinct yet
cooperating RPN's. Our RPN's are trained to be different, but not too
different; doing so yields significant performance improvements over state of
the art for COCO and PASCAL VOC in the very few-shot setting. This effect
appears to be independent of the choice of classifier or dataset.
