Hierarchical Graph Pattern Understanding for Zero-Shot VOS

• URL: http://arxiv.org/abs/2312.09525v1
• Date: Fri, 15 Dec 2023 04:13:21 GMT
• Title: Hierarchical Graph Pattern Understanding for Zero-Shot VOS
• Authors: Gensheng Pei, Fumin Shen, Yazhou Yao, Tao Chen, Xian-Sheng Hua, and Heng-Tao Shen
• Abstract summary: This paper proposes a new hierarchical graph neural network (GNN) architecture 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.
• Score: 102.21052200245457
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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}.

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