关键词: 目标检测, 小样本学习, 元学习策略, 特征增强, Transformer

Abstract: Current few-shot object detection methods based on meta-learning often rely on region proposal networks (RPN) to generate candidate boxes. However, pre-defined boxes typically contain many invalid regions, introducing noise that disrupts the accurate localization of few-shot targets with limited training samples. Additionally, existing single-stage meta-learning algorithms primarily use support information at the feature level to guide query learning, but the limited support samples result in features lacking global consistency, hindering the enhancement of query information and affecting classification and localization accuracy. To address these issues, a second-order meta-learning strategy-based few-shot object detection method (SM-FSOD) was proposed. The SM-FSOD model replaced RPN with Transformer encoder-decoder structures and ResNet-101 as the backbone, forming an end-to-end detection framework. A support-query feature parallel encoder was used in the first meta-learning stage to measure correlations between query features and support information, guiding query discrimination. In the second stage, a global prototype feature integration method was applied, coupling depth-aware query features with support features to enhance perception. Experiments on PASCAL-VOC 2007/2012 showed that SM-FSOD improved AP50 by 1.40%-4.02% compared to RPN-based Meta-FRCNN. On the more challenging COCO dataset, SM-FSOD achieved a 2.70%-3.45% AP50 improvement, even with increased categories.

Key words: object detection; few-shot learning, meta-learning strategy, feature enhancement, Transformer