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G-CAME: Gaussian-Class Activation Mapping Explainer for Object Detectors

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arxiv 2306.03400 v1 pith:ZX7V57H3 submitted 2023-06-06 cs.CV cs.AIcs.LG

G-CAME: Gaussian-Class Activation Mapping Explainer for Object Detectors

Quoc Khanh Nguyen , Truong Thanh Hung Nguyen , Vo Thanh Khang Nguyen , Van Binh Truong , Quoc Hung Cao This is my paper
classification cs.CV cs.AIcs.LG
keywords g-cameobjectactivationdetectionexplainergaussianmappingmethod
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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