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.
@article{arxiv.2404.13417,
title = {Efficient and Concise Explanations for Object Detection with Gaussian-Class Activation Mapping Explainer},
author = {Quoc Khanh Nguyen and Truong Thanh Hung Nguyen and Vo Thanh Khang Nguyen and Van Binh Truong and Tuong Phan and Hung Cao},
journal= {arXiv preprint arXiv:2404.13417},
year = {2024}
}