MASSeg : 2nd Technical Report for 4th PVUW MOSE Track
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908318212030464 |
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| author | Cao, Xuqiang Zhao, Linnan Zhao, Jiaxuan Liu, Fang Chen, Puhua Ma, Wenping |
| author_facet | Cao, Xuqiang Zhao, Linnan Zhao, Jiaxuan Liu, Fang Chen, Puhua Ma, Wenping |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_10254 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MASSeg : 2nd Technical Report for 4th PVUW MOSE Track Cao, Xuqiang Zhao, Linnan Zhao, Jiaxuan Liu, Fang Chen, Puhua Ma, Wenping Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | MASSeg : 2nd Technical Report for 4th PVUW MOSE Track |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2504.10254 |