  Synthetic datasets are often used to pretrain end-to-end optical flow
networks, due to the lack of a large amount of labeled, real-scene data. But
major drops in accuracy occur when moving from synthetic to real scenes. How do
we better transfer the knowledge learned from synthetic to real domains? To
this end, we propose CLIP-FLow, a semi-supervised iterative pseudo-labeling
framework to transfer the pretraining knowledge to the target real domain. We
leverage large-scale, unlabeled real data to facilitate transfer learning with
the supervision of iteratively updated pseudo-ground truth labels, bridging the
domain gap between the synthetic and the real. In addition, we propose a
contrastive flow loss on reference features and the warped features by pseudo
ground truth flows, to further boost the accurate matching and dampen the
mismatching due to motion, occlusion, or noisy pseudo labels. We adopt RAFT as
the backbone and obtain an F1-all error of 4.11%, i.e. a 19% error reduction
from RAFT (5.10%) and ranking 2$^{nd}$ place at submission on the KITTI 2015
benchmark. Our framework can also be extended to other models, e.g. CRAFT,
reducing the F1-all error from 4.79% to 4.66% on KITTI 2015 benchmark.
