  Synthetic images created by image editing operations are prevalent, but the
color or illumination inconsistency between the manipulated region and
background may make it unrealistic. Thus, it is important yet challenging to
localize the inharmonious region to improve the quality of synthetic image.
Inspired by the classic clustering algorithm, we aim to group pixels into two
clusters: inharmonious cluster and background cluster by inserting a novel
Recurrent Self-Reasoning (RSR) module into the bottleneck of UNet structure.
The mask output from RSR module is provided for the decoder as attention
guidance. Finally, we adaptively combine the masks from RSR and the decoder to
form our final mask. Experimental results on the image harmonization dataset
demonstrate that our method achieves competitive performance both
quantitatively and qualitatively.
