  Referring video object segmentation (RVOS) aims to segment video objects with
the guidance of natural language reference. Previous methods typically tackle
RVOS through directly grounding linguistic reference over the image lattice.
Such bottom-up strategy fails to explore object-level cues, easily leading to
inferior results. In this work, we instead put forward a two-stage, top-down
RVOS solution. First, an exhaustive set of object tracklets is constructed by
propagating object masks detected from several sampled frames to the entire
video. Second, a Transformer-based tracklet-language grounding module is
proposed, which models instance-level visual relations and cross-modal
interactions simultaneously and efficiently. Our model ranks first place on
CVPR2021 Referring Youtube-VOS challenge.
