FFAM: Feature Factorization Activation Map for Explanation of 3D Detectors
<|reference_start|>FFAM: Feature Factorization Activation Map for Explanation of 3D Detectors: LiDAR-based 3D object detection has made impressive progress recently, yet most existing models are black-box, lacking interpretability. Previous explanation approaches primarily focus on analyzing image-based models and are not readily applicable to LiDAR-based 3D detectors. In this paper, we propose a feature factorization activation map (FFAM) to generate high-quality visual explanations for 3D detectors. FFAM employs non-negative matrix factorization to generate concept activation maps and subsequently aggregates these maps to obtain a global visual explanation. To achieve object-specific visual explanations, we refine the global visual explanation using the feature gradient of a target object. Additionally, we introduce a voxel upsampling strategy to align the scale between the activation map and input point cloud. We qualitatively and quantitatively analyze FFAM with multiple detectors on several datasets. Experimental results validate the high-quality visual explanations produced by FFAM. The Code will be available at \url{https://github.com/Say2L/FFAM.git}.<|reference_end|>
@article{liu2024ffam:,
    title={FFAM: Feature Factorization Activation Map for Explanation of 3D
  Detectors},
    author={Shuai Liu, Boyang Li, Zhiyu Fang, Mingyue Cui, Kai Huang},
    journal={arXiv preprint arXiv:2405.12601},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.12601},
    primaryClass={cs.CV}
}