  We revisit the one- and two-stage detector distillation tasks and present a
simple and efficient semantic-aware framework to fill the gap between them. We
address the pixel-level imbalance problem by designing the category anchor to
produce a representative pattern for each category and regularize the
topological distance between pixels and category anchors to further tighten
their semantic bonds. We name our method SEA (SEmantic-aware Alignment)
distillation given the nature of abstracting dense fine-grained information by
semantic reliance to well facilitate distillation efficacy. SEA is well adapted
to either detection pipeline and achieves new state-of-the-art results on the
challenging COCO object detection task on both one- and two-stage detectors.
Its superior performance on instance segmentation further manifests the
generalization ability. Both 2x-distilled RetinaNet and FCOS with ResNet50-FPN
outperform their corresponding 3x ResNet101-FPN teacher, arriving 40.64 and
43.06 AP, respectively. Code will be made publicly available.
