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In this paper, we propose an efficient saliency map generation method, calledGroup score-weighted Class Activation Mapping (Group-CAM), which adopts the"split-transform-merge" strategy to generate saliency maps. Specifically, foran input image, the class activations are firstly split into groups. In eachgroup, the sub-activations are summed and de-noised as an initial mask. Afterthat, the initial masks are transformed with meaningful perturbations and thenapplied to preserve sub-pixels of the input (i.e., masked inputs), which arethen fed into the network to calculate the confidence scores. Finally, theinitial masks are weighted summed to form the final saliency map, where theweights are confidence scores produced by the masked inputs. Group-CAM isefficient yet effective, which only requires dozens of queries to the networkwhile producing target-related saliency maps. As a result, Group-CAM can beserved as an effective data augment trick for fine-tuning the networks. Wecomprehensively evaluate the performance of Group-CAM on common-usedbenchmarks, including deletion and insertion tests on ImageNet-1k, and pointinggame tests on COCO2017. Extensive experimental results demonstrate thatGroup-CAM achieves better visual performance than the current state-of-the-artexplanation approaches. The code is available athttps://github.com/wofmanaf/Group-CAM.

Group-CAM: Group Score-Weighted Visual Explanations for Deep Convolutional Networks