  Explaining a classification result produced by an image- and
video-classification model is one of the important but challenging issues in
computer vision. Many methods have been proposed for producing heat-map--based
explanations for this purpose, including ones based on the white-box approach
that uses the internal information of a model (e.g., LRP, Grad-CAM, and
Grad-CAM++) and ones based on the black-box approach that does not use any
internal information (e.g., LIME, SHAP, and RISE). We propose a new black-box
method BOREx (Bayesian Optimization for Refinement of visual model Explanation)
to refine a heat map produced by any method. Our observation is that a
heat-map--based explanation can be seen as a prior for an explanation method
based on Bayesian optimization. Based on this observation, BOREx conducts
Gaussian process regression (GPR) to estimate the saliency of each pixel in a
given image starting from the one produced by another explanation method. Our
experiments statistically demonstrate that the refinement by BOREx improves
low-quality heat maps for image- and video-classification results.
