WSODPB: Weakly supervised object detection with PCSNet and box regression module

Weakly supervised object detection (WSOD) task uses only image-level annotations to train object detection task. WSOD does not require time-consuming instance-level annotations, which attracts more and more attention. Previous weakly supervised object detection methods iteratively update detectors and pseudo-labels or use rules-based methods, which could not generate complete and accurate proposals. We utilize the features extracted by the convolutional layers to optimize the proposals generated by rules-based methods, and solve the above problem through combining the two different features. Then, a box regression module is added to the weakly supervised object detection network, which supervised by a proposal completeness scoring network (PCSNet). The box regression module modifies proposals to obtain higher intersection-over-Union (IoU) with ground truth. PCSNet scores the proposal output from the box regression network and utilizes the score to improve the box regression module. In addition, we take advantage of the random proposal scoring (RPS) algorithm for generating more accurate pseudo labels to train the box regression module. The results show that our methods have made significant improvements on PASCAL VOC 2007 and 2012 datasets.