重新思考目标检测的标注：标注小尺寸实例是否物有所值？
Rethinking Annotation for Object Detection: Is Annotating Small-size Instances Worth Its Cost?
AI总结：
该研究针对小尺寸目标实例标注成本高的问题，探究无小实例训练数据的检测器能否检测小目标，通过测试时放大加域差补救及蒸馏方法，在COCO数据集上达到与全数据训练基线相当的性能，为目标检测标注策略优化提供新思路。
摘要：
检测图像中仅占小面积的目标难度很大，即便对人类而言也是如此。因此，标注小尺寸目标实例难度高，成本也随之高昂。本研究对这一共识提出质疑，探讨如下问题：标注小尺寸实例是否值得付出相应成本？我们将其重新表述为一个可验证的问题：我们能否用不含小尺寸实例的训练数据训练出的检测器，检测出小尺寸实例？我们评估了两种方法：一种是在测试时放大输入图像的方法，另一种是在训练时缩小图像的方法。使用COCO数据集开展的实验得出了以下结果：第一种方法结合缩小训练与测试输入间域差距的补救措施，取得的性能至少与使用完整训练数据训练的基线检测器相当。尽管该方法需要对同一输入图像以不同缩放比例运行两次检测器，但我们证明通过知识蒸馏可得到单路径检测器，其性能与该基线检测器相当。这些结果表明，有必要重新思考目标检测训练数据的标注策略。
英文摘要：
Detecting objects occupying only small areas in an image is difficult, even for humans. Therefore, annotating small-size object instances is hard and thus costly. This study questions common sense by asking the following: is annotating small-size instances worth its cost? We restate it as the following verifiable question: can we detect small-size instances with a detector trained using training data free of small-size instances? We evaluate a method that upscales input images at test time and a method that downscales images at training time. The experiments conducted using the COCO dataset show the following. The first method, together with a remedy to narrow the domain gap between training and test inputs, achieves at least comparable performance to the baseline detector trained using complete training data. Although the method needs to apply the same detector twice to an input image with different scaling, we show that its distillation yields a single-path detector that performs equally well to the same baseline detector. These results point to the necessity of rethinking the annotation of training data for object detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
