  We present a refinement framework to boost the performance of pre-trained
semi-supervised video object segmentation (VOS) models. Our work is based on
scale inconsistency, which is motivated by the observation that existing VOS
models generate inconsistent predictions from input frames with different
sizes. We use the scale inconsistency as a clue to devise a pixel-level
attention module that aggregates the advantages of the predictions from
different-size inputs. The scale inconsistency is also used to regularize the
training based on a pixel-level variance measured by an uncertainty estimation.
We further present a self-supervised online adaptation, tailored for test-time
optimization, that bootstraps the predictions without ground-truth masks based
on the scale inconsistency. Experiments on DAVIS 16 and DAVIS 17 datasets show
that our framework can be generically applied to various VOS models and improve
their performance.
