Abstract
Large-scale Video Object Segmentation (LSVOS) addresses the challenge of accurately tracking and segmenting objects in long video sequences, where difficulties stem from object reappearance, small-scale targets, heavy occlusions, and crowded scenes. Existing approaches predominantly adopt SAM2-based frameworks with various memory mechanisms for complex video mask generation. In this report, we proposed Segment Anything with Memory Strengthened Object Navigation (SAMSON), the 3rd place solution in the MOSE track of ICCV 2025, which integrates the strengths of stateof-the-art VOS models into an effective paradigm. To handle visually similar instances and long-term object disappearance in MOSE, we incorporate a long-term memorymodule for reliable object re-identification. Additionly, we adopt SAM2Long as a post-processing strategy to reduce error accumulation and enhance segmentation stability in long video sequences. Our method achieved a final performance of 0.8427 in terms of J &F in the test-set leaderboard.
Abstract (translated)
大规模视频目标分割（LSVOS）旨在解决在长时间视频序列中准确跟踪和分割对象的挑战，这些挑战包括对象再现、小规模目标、严重遮挡以及拥挤场景等问题。现有方法主要采用带有各种内存机制的SAM2框架来生成复杂视频掩码。在这份报告中，我们提出了一种名为“具有增强记忆功能的对象导航”（Segment Anything with Memory Strengthened Object Navigation, SAMSON）的方法，这是ICCV 2025 MOSE赛道的第三名解决方案。该方法将最先进的VOS模型的优势整合到一个有效的框架内。 为了处理MOSE中视觉相似实例和长时间对象消失的问题，我们引入了一个长期记忆模块来可靠地重新识别对象。此外，我们采用了SAM2Long作为后处理策略以减少误差累积，并增强在长视频序列中的分割稳定性。我们的方法在测试集排行榜上取得了J & F指标为0.8427的最终性能。 这个解决方案展示了如何利用先进的技术手段解决大规模和复杂的视频目标分割问题，同时通过创新的记忆机制解决了长期跟踪中出现的问题。
URL
https://arxiv.org/abs/2509.17500
