  Nowadays, deep neural networks for object detection in images are very
prevalent. However, due to the complexity of these networks, users find it hard
to understand why these objects are detected by models. We proposed Gaussian
Class Activation Mapping Explainer (G-CAME), which generates a saliency map as
the explanation for object detection models. G-CAME can be considered a
CAM-based method that uses the activation maps of selected layers combined with
the Gaussian kernel to highlight the important regions in the image for the
predicted box. Compared with other Region-based methods, G-CAME can transcend
time constraints as it takes a very short time to explain an object. We also
evaluated our method qualitatively and quantitatively with YOLOX on the MS-COCO
2017 dataset and guided to apply G-CAME into the two-stage Faster-RCNN model.
