Classifier Refinement for Weakly Supervised Object Detection with Class-Specific Activation Map

Weakly Supervised Object Detection (WSOD) is a challenging visual understanding task due to the absence of expensive human annotations like bounding boxes and segmentation. Recent WSOD methods usually generate a bounding box for the most contrastive part of the object rather than the entire object. Some methods alleviate the incompleteness problem of detection object with segmentation supplement or classifier refinement. However, segmentation requires high model cost and classifier refinement highly relies on the quality of initial candidate boxes at the beginning. In this paper, we propose a WSOD classifier refinement approach to overcome candidate boxes initialization and high segmentation model cost problems. Our approach can get high-quality class-specific activation maps for the objects and generate nail boxes at the maximum response point of the activation map to suppress incorrect refinement direction. Compared with previous methods, our WSOD classifier refinement approach can achieve 42.1% mAP on PASCAL VOC 2007 benchmarks without high segmentation model cost.