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Referring video object segmentation (RVOS) aims to segment video objects withthe guidance of natural language reference. Previous methods typically tackleRVOS through directly grounding linguistic reference over the image lattice.Such bottom-up strategy fails to explore object-level cues, easily leading toinferior results. In this work, we instead put forward a two-stage, top-downRVOS solution. First, an exhaustive set of object tracklets is constructed bypropagating object masks detected from several sampled frames to the entirevideo. Second, a Transformer-based tracklet-language grounding module isproposed, which models instance-level visual relations and cross-modalinteractions simultaneously and efficiently. Our model ranks first place onCVPR2021 Referring Youtube-VOS challenge.

Rethinking Cross-modal Interaction from a Top-down Perspective for Referring Video Object Segmentation