  Due to the scarcity of sampling data in reality, few-shot object detection
(FSOD) has drawn more and more attention because of its ability to quickly
train new detection concepts with less data. However, there are still failure
identifications due to the difficulty in distinguishing confusable classes. We
also notice that the high standard deviation of average precision reveals the
inconsistent detection performance. To this end, we propose a novel FSOD method
with Refined Contrastive Learning (FSRC). A pre-determination component is
introduced to find out the Resemblance Group from novel classes which contains
confusable classes. Afterwards, Refined Contrastive Learning (RCL) is pointedly
performed on this group of classes in order to increase the inter-class
distances among them. In the meantime, the detection results distribute more
uniformly which further improve the performance. Experimental results based on
PASCAL VOC and COCO datasets demonstrate our proposed method outperforms the
current state-of-the-art research.
