Few-shot Object Detection via Refining Eigenspace

Abstract

Few-shot object detection (FSOD) aims to retain the performance of detector when only given scarce annotated instances. We reckon that its difficulty lies in the fact that the scare positive samples restrict the accurate construction of the eigenspace of involved categories. In this paper, we proposed a novel FSOD detector based on refining the eigenspace, which is implemented through a pure positive augmentation, a full feature mining module and a modified loss function. The pure positive augmentation expands the quantity and enriches the scale distribution of positive samples, inhibiting the expansion of negative samples. The full feature mining module enables the model to mining more information about objects. The modified loss function drives prediction results closer to ground truths. We apply these two improvements to YOLOv4, the representative of one-stage detector, which is termed YOLOv4-FS. On PASCAL VOC and MS COCO datasets, our YOLOv4-FS achieves competitive performance compared with existing progressive detectors.