Data pooling offers various advantages, such as increasing the sample size, improving generalization, reducing sampling bias, and addressing data sparsity and quality, but it is not straightforward and may even be counterproductive. Assessing the effectiveness of pooling datasets in a principled manner is challenging due to the difficulty in estimating the overall information content of individual datasets. Towards this end, we propose incorporating a data source prediction module into standard object detection pipelines. The module runs with minimal overhead during inference time, providing additional information about the data source assigned to individual detections. We show the benefits of the so-called dataset affinity score by automatically selecting samples from a heterogeneous pool of vehicle datasets. The results show that object detectors can be trained on a significantly sparser set of training samples without losing detection accuracy.

翻译：数据池化具有多种优势，例如增加样本量、提升泛化能力、减少采样偏差以及应对数据稀疏性和质量问题，但其应用并非直截了当，甚至可能适得其反。由于难以估计单个数据集的整体信息含量，以原则性方式评估数据池化的有效性颇具挑战。为此，我们提出将数据源预测模块融入标准目标检测流程。该模块在推理阶段以极低开销运行，提供关于单个检测结果所分配数据源的额外信息。通过从异质车辆数据集池中自动选择样本，我们展示了所谓数据集亲和力评分的好处。结果表明，在不损失检测精度的情况下，可以使用显著稀疏的训练样本集来训练目标检测器。