Dual-Awareness Attention for Few-Shot Object Detection

While recent progress has significantly boosted few-shot classification (FSC) performance, few-shot object detection (FSOD) remains challenging for modern learning systems. Existing FSOD systems follow FSC approaches, ignoring the issues of spatial misalignment and vagueness in class representations, and consequently result in low performance. Observing this, we propose a novel Dual-Awareness Attention (DAnA) mechanism that can adaptively generate query-position-aware (QPA) support features and guide the detection networks precisely. The generated QPA features represent local information of a support image conditioned on a given region of the query. By taking the spatial relationships across different images into consideration, our approach conspicuously outperforms previous FSOD methods (+6.9 AP relatively) and achieves remarkable results even under a challenging cross-dataset evaluation setting. Furthermore, the proposed DAnA component is flexible and adaptable to multiple existing object detection frameworks. By equipping DAnA, conventional object detection models, Faster R-CNN and RetinaNet, which are not designed explicitly for few-shot learning, reach state-of-the-art performance in FSOD tasks.