Title: Pseudo-Label Enhanced Cascaded Framework: 2nd Technical Report for LSVOS 2025 VOS Track Show Chinese title

Title: 伪标签增强级联框架：LSVOS 2025 VOS赛道第二份技术报告

Abstract: Complex Video Object Segmentation (VOS) presents significant challenges in accurately segmenting objects across frames, especially in the presence of small and similar targets, frequent occlusions, rapid motion, and complex interactions. In this report, we present our solution for the LSVOS 2025 VOS Track based on the SAM2 framework. We adopt a pseudo-labeling strategy during training: a trained SAM2 checkpoint is deployed within the SAM2Long framework to generate pseudo labels for the MOSE test set, which are then combined with existing data for further training. For inference, the SAM2Long framework is employed to obtain our primary segmentation results, while an open-source SeC model runs in parallel to produce complementary predictions. A cascaded decision mechanism dynamically integrates outputs from both models, exploiting the temporal stability of SAM2Long and the concept-level robustness of SeC. Benefiting from pseudo-label training and cascaded multi-model inference, our approach achieves a J\&F score of 0.8616 on the MOSE test set -- +1.4 points over our SAM2Long baseline -- securing the 2nd place in the LSVOS 2025 VOS Track, and demonstrating strong robustness and accuracy in long, complex video segmentation scenarios. Show Chinese abstract

Abstract: 复杂视频对象分割（VOS）在准确分割帧间对象方面面临重大挑战，尤其是在存在小目标和相似目标、频繁遮挡、快速运动和复杂交互的情况下。 在本报告中，我们基于SAM2框架提出了LSVOS 2025 VOS赛道的解决方案。 我们在训练过程中采用伪标签策略：将训练好的SAM2检查点部署在SAM2Long框架中，以生成MOSE测试集的伪标签，然后将其与现有数据结合进行进一步训练。 在推理阶段，使用SAM2Long框架获得主要分割结果，同时运行一个开源的SeC模型并行生成补充预测。 级联决策机制动态整合两个模型的输出，利用SAM2Long的时间稳定性以及SeC的概念级鲁棒性。 得益于伪标签训练和级联多模型推理，我们的方法在MOSE测试集上实现了J&F得分为0.8616——比我们的SAM2Long基线高出1.4分——在LSVOS 2025 VOS赛道中获得第二名，并在长期、复杂的视频分割场景中表现出强大的鲁棒性和准确性。