Efficient and Concise Explanations for Object Detection with Gaussian-Class Activation Mapping Explainer

• URL: http://arxiv.org/abs/2404.13417v1
• Date: Sat, 20 Apr 2024 16:11:47 GMT
• Title: Efficient and Concise Explanations for Object Detection with Gaussian-Class Activation Mapping Explainer
• Authors: Quoc Khanh Nguyen, Truong Thanh Hung Nguyen, Vo Thanh Khang Nguyen, Van Binh Truong, Tuong Phan, Hung Cao,
• Abstract summary: We introduce the Gaussian Class Activation Mapping Explainer (G-CAME) G-CAME significantly reduces explanation time to 0.5 seconds without compromising the quality. Our evaluation of G-CAME, using Faster-RCNN and YOLOX on the MS-COCO 2017 dataset, demonstrates its ability to offer highly plausible and faithful explanations.
• Score: 3.2766072866432867
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: To address the challenges of providing quick and plausible explanations in Explainable AI (XAI) for object detection models, we introduce the Gaussian Class Activation Mapping Explainer (G-CAME). Our method efficiently generates concise saliency maps by utilizing activation maps from selected layers and applying a Gaussian kernel to emphasize critical image regions for the predicted object. Compared with other Region-based approaches, G-CAME significantly reduces explanation time to 0.5 seconds without compromising the quality. Our evaluation of G-CAME, using Faster-RCNN and YOLOX on the MS-COCO 2017 dataset, demonstrates its ability to offer highly plausible and faithful explanations, especially in reducing the bias on tiny object detection.

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