  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
result in low performance. Observing this, we propose a novel
Dual-Awareness-Attention (DAnA), which captures the pairwise spatial
relationship cross the support and query images. The generated
query-position-aware support features are robust to spatial misalignment and
used to guide the detection network precisely. Our DAnA component is adaptable
to various existing object detection networks and boosts FSOD performance by
paying attention to specific semantics conditioned on the query. Experimental
results demonstrate that DAnA significantly boosts (48% and 125% relatively)
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.
