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 precisions 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 (GR) 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. FSRC can not only decouple
the relevance of confusable classes to get a better performance, but also makes
predictions more consistent by reducing the standard deviation of the AP of
classes to be detected.

通过引入预测组件、改善相似类的区分性以及降低标准差等方式，提出了由精细对比学习和 Resemblance Group 构建的 FSOD 方法，并在 PASCAL VOC 和 COCO 数据集上展示了其优越性。