Weakly Supervised Object Detection Using Class Activation Map

Class activation maps (CAMs) are a crucial tool for visualizing the regions within an image that a classifier uses to identify a given object category. Recent studies have leveraged CAMs for weakly supervised object localization and have achieved promising results. However, they are limited to scenarios with a single object class for each image. This paper introduces a novel framework that extends CAMs for weakly supervised object detection. Experiments on the PASCAL VOC 2007 and 2012 datasets with two backbones, ResNet and VGG, have demonstrated that our method achieves promising results.