用少量标注数据提升无源域适应目标检测，效果远超复杂方法。
Simple Supervision Is Hard to Beat: A Bitter Lesson from Sparse Target Labels in Domain-Adaptive Object Detection
- 引入随机目标监督混合，直接利用少量标注数据优化检测
- 在1%-10%标签预算下，性能提升1.7到18.3个AP50点
- 证明简单监督比复杂伪标签优化更有效，适合实际部署
无源域自适应目标检测（SFDA-OD）通常通过教师-学生自训练和伪标签来适应未标注的目标域。本文在目标域仅少量均匀采样的图像被标注的情况下重新审视该设置，提出随机目标监督混合（RTSM）：通过监督检测损失融入这些标注，同时保持原有无监督适应分支不变。在四种SFDA-OD方法、两种目标检测器、多个适配任务及1%至10%的标签预算下，RTSM始终将纯无源适应性能提升1.7至18.3个AP50。进一步测试了十种基于稀疏标注的反馈插件（涵盖伪标签选择、物体补全与优化控制），但其增益有限且依赖方法。结果揭示了稀疏标注场景下的残酷教训：简单监督难以超越。因此，RTSM为稀疏标注下的SFDA-OD提供了一个简单而有效的基准方案。
原文摘要 · Abstract (English)
Source-free domain adaptive object detection adapts a source-trained detector to an unlabeled target domain, typically through teacher-student self-training with pseudo-labels. We revisit this setting when a small, uniformly sampled subset of target images is labeled. We introduce Random-Target Supervised Mixing (RTSM), a simple anchor that incorporates these annotations through a supervised detection loss while leaving the original unlabeled adaptation branch unchanged. Across evaluations spanning four SFDA-OD methods, two object detectors, multiple adaptation tasks, and target-label budgets from 1% to 10%, RTSM consistently improves pure SFDA by 1.7 to 18.3 AP50. We then examine whether the same annotations can provide further gains by steering unlabeled self-training. To this end, we evaluate ten sparse-label feedback plugins covering pseudo-label selection, object completion, and optimization control, which yield limited and method-dependent gains over RTSM. These results reveal a bitter lesson for sparse-label SFDA-OD: simple supervision is hard to beat. RTSM therefore provides a simple yet effective anchor for sparse-label SFDA-OD.
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