基于对数据集级平均精度边际贡献的目标检测在线数据筛选
Online Data Curation for Object Detection via Marginal Contributions to Dataset-level Average Precision
DetGain 把每张图对数据集级 AP 的边际扰动作为在线采样分数，并用教师-学生贡献差挑样本；做检测训练时可直接套用这套打分器做数据筛选，且与知识蒸馏叠加还能再涨点。
原文依据 estimates the marginal perturbation of each image to dataset-level Average Precision (AP) based on its prediction quality
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论在规模法则下，高质量数据已成为进步的主要驱动力，经过筛选的数据集往往能以更低成本优于规模大得多的未筛选数据集。在线数据筛选通过根据模型不断变化的状态动态选择训练样本，进一步拓展了这一思路。尽管在分类和多模态学习中行之有效，现有在线采样策略却很少能扩展到目标检测，原因在于其结构复杂性和领域差异。我们提出 DetGain，一种专门面向目标检测的在线数据筛选方法，它依据每张图像的预测质量，估计该图像对数据集级平均精度（AP）的边际扰动。通过建模全局分数分布，DetGain 高效地估计全局 AP 变化，并计算教师-学生贡献差，从而在每次迭代时选出信息量大的样本。该方法与架构无关且侵入性极小，可直接集成到多种目标检测架构中。在 COCO 数据集上使用多个代表性检测器进行的实验表明，精度获得一致提升。DetGain 还在低质量数据下展现出较强的鲁棒性，并可与知识蒸馏技术有效结合以进一步提升性能，凸显了其作为面向数据高效目标检测的通用且互补策略的潜力。
英文原文摘要 1393 字符
High-quality data has become a primary driver of progress under scale laws, with curated datasets often outperforming much larger unfiltered ones at lower cost. Online data curation extends this idea by dynamically selecting training samples based on the model's evolving state. While effective in classification and multimodal learning, existing online sampling strategies rarely extend to object detection because of its structural complexity and domain gaps. We introduce DetGain, an online data curation method specifically for object detection that estimates the marginal perturbation of each image to dataset-level Average Precision (AP) based on its prediction quality. By modeling global score distributions, DetGain efficiently estimates the global AP change and computes teacher-student contribution gaps to select informative samples at each iteration. The method is architecture-agnostic and minimally intrusive, enabling straightforward integration into diverse object detection architectures. Experiments on the COCO dataset with multiple representative detectors show consistent improvements in accuracy. DetGain also demonstrates strong robustness under low-quality data and can be effectively combined with knowledge distillation techniques to further enhance performance, highlighting its potential as a general and complementary strategy for data-efficient object detection.