@inproceedings{321558a2186c4b17a6d301424b808333,
title = "Explanations for Occluded Images",
abstract = "Existing algorithms for explaining the output of image classifiers perform poorly on inputs where the object of interest is partially occluded. We present a novel, black-box algorithm for computing explanations that uses a principled approach based on causal theory. We have implemented the method in the DEEPCOVER tool. We obtain explanations that are much more accurate than those generated by the existing explanation tools on images with occlusions and observe a level of performance comparable to the state of the art when explaining images without occlusions.",
author = "Hana Chockler and Daniel Kroening and Youcheng Sun",
year = "2022",
month = feb,
day = "28",
doi = "10.1109/ICCV48922.2021.00127",
language = "English",
series = " IEEE/CVF International Conference on Computer Vision (ICCV): Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 International Conference on Computer Vision: Proceedings",
address = "United States",
}