Gradient-based refined class activation map for weakly supervised object localization

•We propose a novel Gradient-based Refined CAM approach based on the thought of gradients to mine entire object regions. Our GRCAM makes improvements during the testing stage and does not increase huge training resources.•We exploit the gradients of the classification loss function to mine the inter-class relationship among the predicted probabilities. The class-specific mask is generated based on inter-class relations to enhance the information of the target class.•We design a regression function containing the intra-class relationship. The gradients of the regression function are utilized to mine the category consistency for revising the bounding box.