ITFD: an instance-level triplet few-shot detection network under weighted pair-resampling

Few-shot object detection has been widely applied in industrial applications, endangered detection, tumor lesion detection, etc. Although many excellent few-shot detection models have been proposed recently, intra-class inter-class confusion and low activation of novel classes still keep few-shot detection challenging. In this paper, we propose a novel few-shot detection model ITFD, in which a weighted pair-resampling method improves the localization efficiency of the novel-class and a hard triplet loss reduces intra-class inter-class confusion are contained. Extensive experiments have shown that our model achieves 3.6 % , 2.6 % , and 3.7 % average nAP50 improvement on novel-class setup 1,2,3 of PASCAL VOC compared to the same one-time fine-tuning type of models. Besides, to verify the effectiveness of our model in practical application, we established two train component detection datasets. Our model achieves state-of-the-art performance on both datasets with an average nAP50 improvement of 7 % and 4.8 % respectively.