Efficient and Concise Explanations for Object Detection with Gaussian-Class Activation Mapping Explainer
<|reference_start|>Efficient and Concise Explanations for Object Detection with Gaussian-Class Activation Mapping Explainer: 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.<|reference_end|>
@article{nguyen2024efficient,
    title={Efficient and Concise Explanations for Object Detection with
  Gaussian-Class Activation Mapping Explainer},
    author={Quoc Khanh Nguyen, Truong Thanh Hung Nguyen, Vo Thanh Khang Nguyen,
  Van Binh Truong, Tuong Phan, Hung Cao},
    journal={arXiv preprint arXiv:2404.13417},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.13417},
    primaryClass={cs.CV cs.AI}
}