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Efficient and Concise Explanations for Object Detection with Gaussian-Class Activation Mapping Explainer
April 23, 2024, 4:46 a.m. | Quoc Khanh Nguyen, Truong Thanh Hung Nguyen, Vo Thanh Khang Nguyen, Van Binh Truong, Tuong Phan, Hung Cao
cs.CV updates on arXiv.org arxiv.org
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 …
abstract arxiv challenges class cs.ai cs.cv detection explainable ai explainer mapping maps object type xai
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