  Existing matching-based approaches perform video object segmentation (VOS)
via retrieving support features from a pixel-level memory, while some pixels
may suffer from lack of correspondence in the memory (i.e., unseen), which
inevitably limits their segmentation performance. In this paper, we present a
Two-Stream Network (TSN). Our TSN includes (i) a pixel stream with a
conventional pixel-level memory, to segment the seen pixels based on their
pixellevel memory retrieval. (ii) an instance stream for the unseen pixels,
where a holistic understanding of the instance is obtained with dynamic
segmentation heads conditioned on the features of the target instance. (iii) a
pixel division module generating a routing map, with which output embeddings of
the two streams are fused together. The compact instance stream effectively
improves the segmentation accuracy of the unseen pixels, while fusing two
streams with the adaptive routing map leads to an overall performance boost.
Through extensive experiments, we demonstrate the effectiveness of our proposed
TSN, and we also report state-of-the-art performance of 86.1% on YouTube-VOS
2018 and 87.5% on the DAVIS-2017 validation split.
