  We propose an adversarial contextual model for detecting moving objects in
images. A deep neural network is trained to predict the optical flow in a
region using information from everywhere else but that region (context), while
another network attempts to make such context as uninformative as possible. The
result is a model where hypotheses naturally compete with no need for explicit
regularization or hyper-parameter tuning. Although our method requires no
supervision whatsoever, it outperforms several methods that are pre-trained on
large annotated datasets. Our model can be thought of as a generalization of
classical variational generative region-based segmentation, but in a way that
avoids explicit regularization or solution of partial differential equations at
run-time.
