Abstract
In this paper, we propose a simple yet effective method
to learn globally optimized detector for object detection,
which is a simple modification to the standard cross-entropy
gradient inspired by the REINFORCE algorithm. In our
approach, the cross-entropy gradient is adaptively adjusted
according to overall mean Average Precision (mAP) of the
current state for each detection candidate, which leads to
more effective gradient and global optimization of detection results, and brings no computational overhead. Benefiting from more precise gradients produced by the global
optimization method, our framework significantly improves
state-of-the-art object detectors. Furthermore, since our
method is based on scores and bounding boxes without
modification on the architecture of object detector, it can
be easily applied to off-the-shelf modern object detection
frameworks