用最新物体记忆提升视频实例分割的长期跟踪一致性
Latest Object Memory Management for Temporally Consistent Video Instance Segmentation
- 引入最新物体记忆机制，实时更新每帧物体状态
- 在YouTube-VIS 2022上达54.0的AP，刷新纪录
- 适合需要稳定身份追踪的动态场景应用
本文提出最新的物体记忆管理（LOMM）方法，用于提升视频实例分割中的长期时序一致性。核心是最新物体记忆（LOM），通过显式建模物体在每一帧中的存在状态，实现对物体状态的鲁棒追踪与持续更新，从而增强跨帧的身份管理能力。此外，提出解耦物体关联（DOA）策略，分别处理新出现和已存在物体，借助内存系统精准分配物体索引，显著提升匹配准确率，确保复杂动态场景下身份一致性的稳定性。大量实验与消融研究验证了该方法的优势，尤其在具有挑战性的长视频数据集YouTube-VIS 2022上，取得54.0的领先AP得分，树立新基准。
原文摘要 · Abstract (English)
In this paper, we present Latest Object Memory Management (LOMM) for temporally consistent video instance segmentation that significantly improves long-term instance tracking. At the core of our method is Latest Object Memory (LOM), which robustly tracks and continuously updates the latest states of objects by explicitly modeling their presence in each frame. This enables consistent tracking and accurate identity management across frames, enhancing both performance and reliability through the VIS process. Moreover, we introduce Decoupled Object Association (DOA), a strategy that separately handles newly appearing and already existing objects. By leveraging our memory system, DOA accurately assigns object indices, improving matching accuracy and ensuring stable identity consistency, even in dynamic scenes where objects frequently appear and disappear. Extensive experiments and ablation studies demonstrate the superiority of our method over traditional approaches, setting a new benchmark in VIS. Notably, our LOMM achieves state-of-the-art AP score of 54.0 on YouTube-VIS 2022, a dataset known for its challenging long videos. Project page: https://seung-hun-lee.github.io/projects/LOMM/
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