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Efficient and Concise Explanations for Object Detection with Gaussian-Class Activation Mapping Explainer
- Publication Year :
- 2024
-
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.<br />Comment: Canadian AI 2024
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2404.13417
- Document Type :
- Working Paper