  We propose a dual system for unsupervised object segmentation in video, which
brings together two modules with complementary properties: a space-time graph
that discovers objects in videos and a deep network that learns powerful object
features. The system uses an iterative knowledge exchange policy. A novel
spectral space-time clustering process on the graph produces unsupervised
segmentation masks passed to the network as pseudo-labels. The net learns to
segment in single frames what the graph discovers in video and passes back to
the graph strong image-level features that improve its node-level features in
the next iteration. Knowledge is exchanged for several cycles until
convergence. The graph has one node per each video pixel, but the object
discovery is fast. It uses a novel power iteration algorithm computing the main
space-time cluster as the principal eigenvector of a special Feature-Motion
matrix without actually computing the matrix. The thorough experimental
analysis validates our theoretical claims and proves the effectiveness of the
cyclical knowledge exchange. We also perform experiments on the supervised
scenario, incorporating features pretrained with human supervision. We achieve
state-of-the-art level on unsupervised and supervised scenarios on four
challenging datasets: DAVIS, SegTrack, YouTube-Objects, and DAVSOD.
