This paper presents a detection-aware pre-training (DAP) approach, which
leverages only weakly-labeled classification-style datasets (e.g., ImageNet)
for pre-training, but is specifically tailored to benefit object detection
tasks. In contrast to the widely used image classification-based pre-training
(e.g., on ImageNet), which does not include any location-related training
tasks, we transform a classification dataset into a detection dataset through a
weakly supervised object localization method based on Class Activation Maps to
directly pre-train a detector, making the pre-trained model location-aware and
capable of predicting bounding boxes. We show that DAP can outperform the
traditional classification pre-training in terms of both sample efficiency and
convergence speed in downstream detection tasks including VOC and COCO. In
particular, DAP boosts the detection accuracy by a large margin when the number
of examples in the downstream task is small.

本文提出了一种检测感知的预训练方法，使用弱标记分类样本集（如 ImageNet）进行预训练，但该方法是专门针对目标检测任务进行设计的，并通过一种基于类激活图的弱监督目标定位方法将分类数据集转换为检测数据集以直接预训练检测器，使得预训练模型具备了位置感知能力和边界框预测能力，结果表明该方法在 VOC 和 COCO 等下游检测任务中的样本效率和收敛速度方面均优于传统的基于分类的预训练方法，在下游任务样本较少时，该方法能大幅度提高检测精度。