Few-shot object detection based on positive-sample improvement

Abstract

Traditional object detectors based on deep learning rely on plenty of labeled samples, which are expensive to obtain. Few-shot object detection (FSOD) attempts to solve this problem, learning detection objects from a few labeled samples, but the performance is often unsatisfactory due to the scarcity of samples. We believe that the main reasons that restrict the performance of few-shot detectors are: (1) the positive samples is scarce, and (2) the quality of positive samples is low. Therefore, we put forward a novel few-shot object detector based on YOLOv4, starting from both improving the quantity and quality of positive samples. First, we design a hybrid multivariate positive sample augmentation (HMPSA) module to amplify the quantity of positive samples and increase positive sample diversity while suppressing negative samples. Then, we design a selective non-local fusion attention (SNFA) module to help the detector better learn the target features and improve the feature quality of positive samples. Finally, we optimize the loss function to make it more suitable for the task of FSOD. Experimental results on PASCAL VOC and MS COCO demonstrate that our designed few-shot object detector has competitive performance with other state-of-the-art detectors.

Keywords

• Few-shot learning
• Object detection
• Sample augmentation
• Attention mechanism