  The objective of the image inpainting task is to fill missing regions of an
image in a visually plausible way. Recently, deep-learning-based image
inpainting networks have generated outstanding results, and some utilize their
models as object removers by masking unwanted objects in an image. However,
while trying to better remove objects using their networks, the previous works
pay less attention to the importance of the input mask. In this paper, we focus
on generating the input mask to better remove objects using the off-the-shelf
image inpainting network. We propose an automatic mask generator inspired by
the explainable AI (XAI) method, whose output can better remove objects than a
semantic segmentation mask. The proposed method generates an importance map
using randomly sampled input masks and quantitatively estimated scores of the
completed images obtained from the random masks. The output mask is selected by
a judge module among the candidate masks which are generated from the
importance map. We design the judge module to quantitatively estimate the
quality of the object removal results. In addition, we empirically find that
the evaluation methods used in the previous works reporting object removal
results are not appropriate for estimating the performance of an object
remover. Therefore, we propose new evaluation metrics (FID$^*$ and U-IDS$^*$)
to properly evaluate the quality of object removers. Experiments confirm that
our method shows better performance in removing target class objects than the
masks generated from the semantic segmentation maps, and the two proposed
metrics make judgments consistent with humans.
