SEA: Bridging the Gap Between One- and Two-stage Detector Distillation via SEmantic-aware Alignment

• URL: http://arxiv.org/abs/2203.00862v1
• Date: Wed, 2 Mar 2022 04:24:05 GMT
• Title: SEA: Bridging the Gap Between One- and Two-stage Detector Distillation via SEmantic-aware Alignment
• Authors: Yixin Chen, Zhuotao Tian, Pengguang Chen, Shu Liu, Jiaya Jia
• Abstract summary: We name our method SEA (SEmantic-aware Alignment) distillation given the nature of abstracting dense fine-grained information. It achieves new state-of-the-art results on the challenging object detection task on both one- and two-stage detectors.
• Score: 76.80165589520385
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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