  Object detection is an import task of computer vision.A variety of methods
have been proposed,but methods using the weak labels still do not have a
satisfactory result.In this paper,we propose a new framework that using the
weakly supervised method's output as the pseudo-strong labels to train a
strongly supervised model.One weakly supervised method is treated as black-box
to generate class-specific bounding boxes on train dataset.A de-noise method is
then applied to the noisy bounding boxes.Then the de-noised pseudo-strong
labels are used to train a strongly object detection network.The whole
framework is still weakly supervised because the entire process only uses the
image-level labels.The experiment results on PASCAL VOC 2007 prove the validity
of our framework, and we get result 43.4% on mean average precision compared to
39.5% of the previous best result and 34.5% of the initial
method,respectively.And this frame work is simple and distinct,and is promising
to be applied to other method easily.
