Complex video object segmentation continues to face significant challenges in small object recognition, occlusion handling, and dynamic scene modeling. This report presents our solution, which ranked second in the MOSE track of CVPR 2025 PVUW Challenge. Based on an existing segmentation framework, we propose an improved model named MASSeg for complex video object segmentation, and construct an enhanced dataset, MOSE+, which includes typical scenarios with occlusions, cluttered backgrounds, and small target instances. During training, we incorporate a combination of inter-frame consistent and inconsistent data augmentation strategies to improve robustness and generalization. During inference, we design a mask output scaling strategy to better adapt to varying object sizes and occlusion levels. As a result, MASSeg achieves a J score of 0.8250, F score of 0.9007, and a J&F score of 0.8628 on the MOSE test set.

本研究解决了复杂视频物体分割中的小物体识别、遮挡处理和动态场景建模等重大挑战。提出的MASSeg模型基于现有框架，配合新构建的MOSE+数据集，通过数据增强策略和掩膜输出缩放策略，显著提升了模型的鲁棒性和一般化能力，最终在MOSE测试集上取得了优异的指标表现。

MASSeg：第四届PVUW MOSE赛道第二技术报告