  Combining CNN with CRF for modeling dependencies between pixel labels is a
popular research direction. This task is far from trivial, especially if
end-to-end training is desired. In this paper, we propose a novel simple
approach to CNN+CRF combination. In particular, we propose to simulate a CRF
regularizer with a trainable module that has standard CNN architecture. We call
this module a CRF Simulator. We can automatically generate an unlimited amount
of ground truth for training such CRF Simulator without any user interaction,
provided we have an efficient algorithm for optimization of the actual CRF
regularizer. After our CRF Simulator is trained, it can be directly
incorporated as part of any larger CNN architecture, enabling a seamless
end-to-end training. In particular, the other modules can learn parameters that
are more attuned to the performance of the CRF Simulator module. We demonstrate
the effectiveness of our approach on the task of salient object segmentation
regularized with the standard binary CRF energy. In contrast to previous work
we do not need to develop and implement the complex mechanics of optimizing a
specific CRF as part of CNN. In fact, our approach can be easily extended to
other CRF energies, including multi-label. To the best of our knowledge we are
the first to study the question of whether the output of CNNs can have
regularization properties of CRFs.
