关键词: 小样本目标检测, 元学习, 代理原型蒸馏, 小波卷积, 多关系融合

Abstract: Existing few-shot object detection (FSOD) algorithms often suffer from insufficient class-level prototype generation accuracy and loss of detail information, which limits the feature representation capacity in target regions. To address this issue, a method called Agent Prototypes Aggregation (APA) for FSOD is proposed. The proposed method utilizes agent attention to distill detail-rich prototypes from the support features, which are then accurately assigned to the query feature map based on their correlation, significantly enhancing the feature representation in target instance regions. Additionally, a Wavelet Convolution Enhancement Module (WCEM) and an Adaptive Multi-Relation Fusion Module (AMRF) are designed to optimize global feature extraction and high-level feature fusion, respectively. Experimental results demonstrate that, on the PASCAL VOC dataset with three novel class splits,APA-FSOD achieves a maximum nAP50 improvement of 1.1 percentage points compared to the baseline VFA (Variational Feature Aggregation). On the MS COCO dataset under the 30-shot setting, nAP is improved by 1 percentage point compared to the meta-learning method SMPCCNet (Support-query Mutual Promotion and Classification Correction Network), significantly enhancing the accuracy of few-shot object detection.

Key words: few-shot object detection, meta learning, agent prototypes distillation, wavelet convolution, multi-relation fusion