  The optical flow guidance strategy is ideal for obtaining motion information
of objects in the video. It is widely utilized in video segmentation tasks.
However, existing optical flow-based methods have a significant dependency on
optical flow, which results in poor performance when the optical flow
estimation fails for a particular scene. The temporal consistency provided by
the optical flow could be effectively supplemented by modeling in a structural
form. This paper proposes a new hierarchical graph neural network (GNN)
architecture, dubbed hierarchical graph pattern understanding (HGPU), for
zero-shot video object segmentation (ZS-VOS). Inspired by the strong ability of
GNNs in capturing structural relations, HGPU innovatively leverages motion cues
(\ie, optical flow) to enhance the high-order representations from the
neighbors of target frames. Specifically, a hierarchical graph pattern encoder
with message aggregation is introduced to acquire different levels of motion
and appearance features in a sequential manner. Furthermore, a decoder is
designed for hierarchically parsing and understanding the transformed
multi-modal contexts to achieve more accurate and robust results. HGPU achieves
state-of-the-art performance on four publicly available benchmarks (DAVIS-16,
YouTube-Objects, Long-Videos and DAVIS-17). Code and pre-trained model can be
found at \url{https://github.com/NUST-Machine-Intelligence-Laboratory/HGPU}.
