蜜蜂在哪里？用单协作头Transformer检测小型传粉昆虫
Where Is the Bee? Detecting Tiny Pollinators with a Single Collaborative-Head Transformer
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中文总结 AI 辅助
针对ECCV 2026 BuzzSpot挑战赛中小型传粉昆虫检测的难点，采用带Swin-L骨干的Co-DINO，结合裁剪马赛克池微调与类别加权ETF损失，在无推理增强下获挑战赛第一。
中文摘要 AI 辅助
CVPPA@ECCV 2026 BuzzSpot挑战赛要求在1920×1080的野外关键帧中检测蜜蜂、熊蜂、食蚜蝇和飞蛾，其标注存在两个难点：标注框的中位数占帧的0.16%，且蜜蜂占标注的80%。为应对小型标注框，我们在保留的关键帧上对比10种已记录的检测器配置，带有Swin-L骨干网络的纯Co-DINO在该对比中具有最高的mAP，因此我们选择它。训练阶段通过两种方式解决蜜蜂占主导的问题：一是在裁剪马赛克池上进行微调，其中3个稀有类别的组合标注占比从19.9%提升至55.1%；二是采用类别加权的单纯形等角紧框架（ETF）损失，该损失会将匹配的解码器查询的投影状态拉向固定的类别方向。完整训练计划为12+3+2个epoch。在不使用推理时集成或测试时增强的情况下，我们在FinalTest上以0.5062的mAP@[.5:.95]排名第一。
英文摘要
The CVPPA@ECCV 2026 BuzzSpot Challenge asks us to detect bees, bumblebees, hoverflies, and moths in 1920x1080 field keyframes. Its annotations carry 2 difficulties: the median box occupies 0.16% of a frame, and bees account for 80% of the labels. To cope with the small boxes, we compare 10 recorded detector configurations on held-out keyframes; plain Co-DINO with a Swin-L backbone has the highest mAP in this comparison, so we select it. Training then addresses the bee dominance in 2 ways: fine-tuning on a crop-mosaic pool in which the combined annotation share of the 3 rare classes rises from 19.9% to 55.1%, and a class-weighted simplex equiangular tight frame (ETF) loss that pulls the projected states of matched decoder queries toward fixed class directions. The full schedule spans 12+3+2 epochs. Without inference-time ensembling or test-time augmentation, we rank first on FinalTest at 0.5062 mAP@[.5:.95].
发表机构
- UNIST（蔚山科学技术院）
机构由 AI 辅助整理，请以论文原文为准。
