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.
View on arXiv@article{cao2025_2504.10254,
title={ MASSeg : 2nd Technical Report for 4th PVUW MOSE Track },
author={ Xuqiang Cao and Linnan Zhao and Jiaxuan Zhao and Fang Liu and Puhua Chen and Wenping Ma },
journal={arXiv preprint arXiv:2504.10254},
year={ 2025 }
}