It is a common paradigm in object detection frameworks to treat all samples equally and target at maximizing the performance on average. In this work, we revisit this paradigm through a careful study on how different samples contribute to the overall performance measured in terms of mAP. Our study suggests that the samples in each mini-batch are neither independent nor equally important, and therefore a better classifier on average does not necessarily mean higher mAP. Motivated by this study, we propose the notion of Prime Samples, those that play a key role in driving the detection performance. We further develop a simple yet effective sampling and learning strategy called PrIme Sample Attention (PISA) that directs the focus of the training process towards such samples. Our experiments demonstrate that it is often more effective to focus on prime samples than hard samples when training a detector. Particularly, On the MSCOCO dataset, PISA outperforms the random sampling baseline and hard mining schemes, e.g. OHEM and Focal Loss, consistently by more than 1% on both single-stage and two-stage detectors, with a strong backbone ResNeXt-101.

翻译：在这项工作中,我们通过仔细研究不同样品如何有助于按 mAP 测量的整体性能,重新审视这一模式。我们的研究显示,每个小型批次的样品既不独立,也不具有同等重要性,因此平均而言,一个更好的分类器并不一定意味着更高的 mAP。我们根据这项研究,提出了原始样品的概念,这些样品在推动探测性能方面起着关键作用。我们进一步制定了一个简单而有效的取样和学习战略,称为PrIme 抽样注意(PISA),将培训过程的重点放在这类样品上。我们的实验表明,在训练探测器时,重点研究原始样品往往比硬样品更有效。特别是,在MSCCO数据集方面,PISA超过随机抽样基线和硬采矿计划,例如,OHEM和Colnal Loss,在单级和两阶段的探测器上始终以1%以上的比例进行,并有一个强大的ResNeXt-101骨架。