  We propose MONet, a convolutional neural network that jointly detects motion
boundaries (MBs) and occlusion regions (Occs) in video both forward and
backward in time. Detection is difficult because optical flow is discontinuous
along MBs and undefined in Occs, while many flow estimators assume smoothness
and a flow defined everywhere. To reason in the two time directions
simultaneously, we direct-warp the estimated maps between the two frames. Since
appearance mismatches between frames often signal vicinity to MBs or Occs, we
construct a cost block that for each feature in one frame records the lowest
discrepancy with matching features in a search range. This cost block is
two-dimensional, and much less expensive than the four-dimensional cost volumes
used in flow analysis. Cost-block features are computed by an encoder, and MB
and Occ estimates are computed by a decoder. We found that arranging decoder
layers fine-to-coarse, rather than coarse-to-fine, improves performance. MONet
outperforms the prior state of the art for both tasks on the Sintel and
FlyingChairsOcc benchmarks without any fine-tuning on them.
