  Existing state-of-the-art methods for Video Object Segmentation (VOS) learn
low-level pixel-to-pixel correspondences between frames to propagate object
masks across video. This requires a large amount of densely annotated video
data, which is costly to annotate, and largely redundant since frames within a
video are highly correlated. In light of this, we propose HODOR: a novel method
that tackles VOS by effectively leveraging annotated static images for
understanding object appearance and scene context. We encode object instances
and scene information from an image frame into robust high-level descriptors
which can then be used to re-segment those objects in different frames. As a
result, HODOR achieves state-of-the-art performance on the DAVIS and
YouTube-VOS benchmarks compared to existing methods trained without video
annotations. Without any architectural modification, HODOR can also learn from
video context around single annotated video frames by utilizing cyclic
consistency, whereas other methods rely on dense, temporally consistent
annotations. Source code is available at: https://github.com/Ali2500/HODOR
