CFA-FSOD: Context-aware Feature Aggregation for Few-Shot Object Detection

Few-shot object detection FSOD aims to detect novel categories from only a few labeled samples. Most of the meta-learning based FSOD methods tend to rely on static support features which lack adaptability to query contexts and have limited representational power, and they often underutilize class-specific features to refine proposals to promote detection performance. To address these challenges, we propose a novel Context-aware Feature Aggregation for FSOD CFA-FSOD that enhances interaction in a support-query bidirectional manner. Concretely, in this method, a Query-guided Support Enhancement QSE module is proposed to adaptively integrate features from query image regions typically proposals into support features to enhance their flexibility meanwhile, a Cross-attention Feature Modulation CFM module is proposed to leverage the enhanced support features to refine query proposals for fine-grained alignment. Experimental results on both Pascal VOC and MS COCO demonstrate that CFA-FSOD achieves outstanding performance in most evaluation settings, benefiting from its bidirectional interaction mechanism that improves the efficiency of sample utilization and the transfer of category-specific features.