3rd Place Solution for PVUW Challenge 2024: Video Panoptic Segmentation
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911909897306112 |
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| author | Wu, Ruipu Che, Jifei Li, Han Wu, Chengjing Liu, Ting Liu, Luoqi |
| author_facet | Wu, Ruipu Che, Jifei Li, Han Wu, Chengjing Liu, Ting Liu, Luoqi |
| contents | Video panoptic segmentation is an advanced task that extends panoptic segmentation by applying its concept to video sequences. In the hope of addressing the challenge of video panoptic segmentation in diverse conditions, We utilize DVIS++ as our baseline model and enhance it by introducing a comprehensive approach centered on the query-wise ensemble, supplemented by additional techniques. Our proposed approach achieved a VPQ score of 57.01 on the VIPSeg test set, and ranked 3rd in the VPS track of the 3rd Pixel-level Video Understanding in the Wild Challenge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_04002 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 3rd Place Solution for PVUW Challenge 2024: Video Panoptic Segmentation Wu, Ruipu Che, Jifei Li, Han Wu, Chengjing Liu, Ting Liu, Luoqi Computer Vision and Pattern Recognition Video panoptic segmentation is an advanced task that extends panoptic segmentation by applying its concept to video sequences. In the hope of addressing the challenge of video panoptic segmentation in diverse conditions, We utilize DVIS++ as our baseline model and enhance it by introducing a comprehensive approach centered on the query-wise ensemble, supplemented by additional techniques. Our proposed approach achieved a VPQ score of 57.01 on the VIPSeg test set, and ranked 3rd in the VPS track of the 3rd Pixel-level Video Understanding in the Wild Challenge. |
| title | 3rd Place Solution for PVUW Challenge 2024: Video Panoptic Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.04002 |