LSVOS Challenge 3rd Place Report: SAM2 and Cutie based VOS
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913475230433280 |
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| author | Liu, Xinyu Zhang, Jing Zhang, Kexin Liu, Xu Li, Lingling |
| author_facet | Liu, Xinyu Zhang, Jing Zhang, Kexin Liu, Xu Li, Lingling |
| contents | Video Object Segmentation (VOS) presents several challenges, including object occlusion and fragmentation, the dis-appearance and re-appearance of objects, and tracking specific objects within crowded scenes. In this work, we combine the strengths of the state-of-the-art (SOTA) models SAM2 and Cutie to address these challenges. Additionally, we explore the impact of various hyperparameters on video instance segmentation performance. Our approach achieves a J\&F score of 0.7952 in the testing phase of LSVOS challenge VOS track, ranking third overall. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_10469 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LSVOS Challenge 3rd Place Report: SAM2 and Cutie based VOS Liu, Xinyu Zhang, Jing Zhang, Kexin Liu, Xu Li, Lingling Computer Vision and Pattern Recognition Information Retrieval Video Object Segmentation (VOS) presents several challenges, including object occlusion and fragmentation, the dis-appearance and re-appearance of objects, and tracking specific objects within crowded scenes. In this work, we combine the strengths of the state-of-the-art (SOTA) models SAM2 and Cutie to address these challenges. Additionally, we explore the impact of various hyperparameters on video instance segmentation performance. Our approach achieves a J\&F score of 0.7952 in the testing phase of LSVOS challenge VOS track, ranking third overall. |
| title | LSVOS Challenge 3rd Place Report: SAM2 and Cutie based VOS |
| topic | Computer Vision and Pattern Recognition Information Retrieval |
| url | https://arxiv.org/abs/2408.10469 |