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arXiv · 2404.13417

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

Quoc Khanh Nguyen·Truong Thanh Hung Nguyen·Vo Thanh Khang Nguyen·Van Binh Truong·Tuong Phan·Hung Cao
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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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Quoc Khanh Nguyen, Truong Thanh Hung Nguyen, Vo Thanh Khang Nguyen, Van Binh Truong, Tuong Phan, Hung Cao. 2024-04-20. Efficient and Concise Explanations for Object Detection with Gaussian-Class Activation Mapping Explainer. https://arxiv.org/abs/2404.13417

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