Cooperating RPN's Improve Few-Shot Object Detection

Weilin Zhang, Yu-Xiong Wang, David Forsyth

Keywords: Few-shot learning, Object detection

Abstract: 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.

One-sentence Summary: We identify the proposal neglect effect in few-shot detection and propose cooperating RPN's that yield significant performance improvements over state of the art in the very few-shot setting.

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