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, neglect the problem of spatial misalignment and the risk of information entanglement, and consequently result in low performance. Observing this, we propose a novel Dual-Awareness Attention (DAnA), which captures the pairwise spatial relationship across the support and query images. The generated query-position-aware (QPA) support features are robust to spatial misalignment and capable of guiding the detection network precisely. Our DAnA component is adaptable to various existing object detection networks and enhances FSOD performance by paying attention to specific semantics conditioned on the query. Experimental results demonstrate that DAnA significantly boosts (+6.9 AP relatively) few- shot object detection performance on the COCO benchmark. By equipping DAnA, conventional object detection models, Faster- RCNN and RetinaNet, which are not designed explicitly for few- shot learning, reach state-of-the-art performance in FSOD tasks.