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.

翻译：由于现实场景中采样数据的稀缺性，小样本目标检测（FSOD）因能利用少量数据快速训练新检测概念而受到越来越多的关注。然而，由于难以区分易混淆类别，仍存在识别失败的问题。我们还注意到，平均精度的标准差较高，反映出检测性能的不一致性。为此，我们提出了一种基于改进对比学习（RCL）的新型FSOD方法（FSRC）。该方法引入预判组件，从包含易混淆类别的新颖类别中找出相似群组。随后，针对该组类别重点应用改进对比学习（RCL），以增大类别间的类间距离。同时，检测结果分布更加均匀，进一步提升了性能。基于PASCAL VOC和COCO数据集的实验结果表明，我们提出的方法优于当前最先进的研究成果。