  Few-shot object detection has gained significant attention in recent years as
it has the potential to greatly reduce the reliance on large amounts of
manually annotated bounding boxes. While most existing few-shot object
detection literature primarily focuses on bounding box classification by
obtaining as discriminative feature embeddings as possible, we emphasize the
necessity of handling the lack of intersection-over-union (IoU) variations
induced by a biased distribution of novel samples. In this paper, we analyze
the IoU imbalance that is caused by the relatively high number of low-quality
region proposals, and reveal that it plays a critical role in improving
few-shot learning capabilities. The well-known two stage fine-tuning technique
causes insufficient quality and quantity of the novel positive samples, which
hinders the effective object detection of unseen novel classes. To alleviate
this issue, we present a few-shot object detection model with proposal balance
refinement, a simple yet effective approach in learning object proposals using
an auxiliary sequential bounding box refinement process. This process enables
the detector to be optimized on the various IoU scores through additional novel
class samples. To fully exploit our sequential stage architecture, we revise
the fine-tuning strategy and expose the Region Proposal Network to the novel
classes in order to provide increased learning opportunities for the
region-of-interest (RoI) classifiers and regressors. Our extensive assessments
on PASCAL VOC and COCO demonstrate that our framework substantially outperforms
other existing few-shot object detection approaches.
