  This paper presents a new adversarial training framework for image inpainting
with segmentation confusion adversarial training (SCAT) and contrastive
learning. SCAT plays an adversarial game between an inpainting generator and a
segmentation network, which provides pixel-level local training signals and can
adapt to images with free-form holes. By combining SCAT with standard global
adversarial training, the new adversarial training framework exhibits the
following three advantages simultaneously: (1) the global consistency of the
repaired image, (2) the local fine texture details of the repaired image, and
(3) the flexibility of handling images with free-form holes. Moreover, we
propose the textural and semantic contrastive learning losses to stabilize and
improve our inpainting model's training by exploiting the feature
representation space of the discriminator, in which the inpainting images are
pulled closer to the ground truth images but pushed farther from the corrupted
images. The proposed contrastive losses better guide the repaired images to
move from the corrupted image data points to the real image data points in the
feature representation space, resulting in more realistic completed images. We
conduct extensive experiments on two benchmark datasets, demonstrating our
model's effectiveness and superiority both qualitatively and quantitatively.
