Object detection aims at high speed and accuracy simultaneously. However, fast models are usually less accurate, while accurate models cannot satisfy our need for speed. A fast model can be 10 times faster but 50\% less accurate than an accurate model. In this paper, we propose Adaptive Feeding (AF) to combine a fast (but less accurate) detector and an accurate (but slow) detector, by adaptively determining whether an image is easy or hard and choosing an appropriate detector for it. In practice, we build a cascade of detectors, including the AF classifier which make the easy vs. hard decision and the two detectors. The AF classifier can be tuned to obtain different tradeoff between speed and accuracy, which has negligible training time and requires no additional training data. Experimental results on the PASCAL VOC, MS COCO and Caltech Pedestrian datasets confirm that AF has the ability to achieve comparable speed as the fast detector and comparable accuracy as the accurate one at the same time. As an example, by combining the fast SSD300 with the accurate SSD500 detector, AF leads to 50\% speedup over SSD500 with the same precision on the VOC2007 test set.

本文利用自适应饲养方法（AF）结合快速和精确的检测器，通过自适应决定图像是易于还是难以检测，构建了级联检测器，包括AF分类器以及两个检测器，以在速度和准确性之间取得平衡，在 PASCAL VOC、MS COCO 和 Caltech 行人数据集上进行了实验，并得到了可比较的速度和准确度。

自适应饲喂：通过自适应组合目标检测器实现快速准确识别