  In this paper, we propose an efficient saliency map generation method, called
Group score-weighted Class Activation Mapping (Group-CAM), which adopts the
"split-transform-merge" strategy to generate saliency maps. Specifically, for
an input image, the class activations are firstly split into groups. In each
group, the sub-activations are summed and de-noised as an initial mask. After
that, the initial masks are transformed with meaningful perturbations and then
applied to preserve sub-pixels of the input (i.e., masked inputs), which are
then fed into the network to calculate the confidence scores. Finally, the
initial masks are weighted summed to form the final saliency map, where the
weights are confidence scores produced by the masked inputs. Group-CAM is
efficient yet effective, which only requires dozens of queries to the network
while producing target-related saliency maps. As a result, Group-CAM can be
served as an effective data augment trick for fine-tuning the networks. We
comprehensively evaluate the performance of Group-CAM on common-used
benchmarks, including deletion and insertion tests on ImageNet-1k, and pointing
game tests on COCO2017. Extensive experimental results demonstrate that
Group-CAM achieves better visual performance than the current state-of-the-art
explanation approaches. The code is available at
https://github.com/wofmanaf/Group-CAM.
