  In this paper, we present a CNN-based fully unsupervised method for motion
segmentation from optical flow. We assume that the input optical flow can be
represented as a piecewise set of parametric motion models, typically, affine
or quadratic motion models. The core idea of our work is to leverage the
Expectation-Maximization (EM) framework in order to design in a well-founded
manner a loss function and a training procedure of our motion segmentation
neural network that does not require either ground-truth or manual annotation.
However, in contrast to the classical iterative EM, once the network is
trained, we can provide a segmentation for any unseen optical flow field in a
single inference step and without estimating any motion models. We investigate
different loss functions including robust ones and propose a novel efficient
data augmentation technique on the optical flow field, applicable to any
network taking optical flow as input. In addition, our method is able by design
to segment multiple motions. Our motion segmentation network was tested on four
benchmarks, DAVIS2016, SegTrackV2, FBMS59, and MoCA, and performed very well,
while being fast at test time.
