  In design of instance segmentation networks that reconstruct masks,
segmentation is often taken as its literal definition -- assigning each pixel a
label. This has led to thinking the problem as a template matching one with the
goal of minimizing the loss between the reconstructed and the ground truth
pixels. Rethinking reconstruction networks as a generator, we define the
problem of predicting masks as a GANs game framework: A segmentation network
generates the masks, and a discriminator network decides on the quality of the
masks. To demonstrate this game, we show effective modifications on the general
segmentation framework in Mask R-CNN. We find that playing the game in feature
space is more effective than the pixel space leading to stable training between
the discriminator and the generator, predicting object coordinates should be
replaced by predicting contextual regions for objects, and overall the
adversarial loss helps the performance and removes the need for any custom
settings per different data domain. We test our framework in various domains
and report on cellphone recycling, autonomous driving, large-scale object
detection, and medical glands. We observe in general GANs yield masks that
account for crispier boundaries, clutter, small objects, and details, being in
domain of regular shapes or heterogeneous and coalescing shapes. Our code for
reproducing the results is available publicly.
