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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.17005 |
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| _version_ | 1866913403210039296 |
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| author | Ding, Henghui Liu, Chang Wei, Yunchao Ravi, Nikhila He, Shuting Bai, Song Torr, Philip Miao, Deshui Li, Xin He, Zhenyu Wang, Yaowei Yang, Ming-Hsuan Xu, Zhensong Yao, Jiangtao Wu, Chengjing Liu, Ting Liu, Luoqi Liu, Xinyu Zhang, Jing Zhang, Kexin Yang, Yuting Jiao, Licheng Yang, Shuyuan Gao, Mingqi Luo, Jingnan Yang, Jinyu Han, Jungong Zheng, Feng Cao, Bin Zhang, Yisi Lin, Xuanxu He, Xingjian Zhao, Bo Liu, Jing Pan, Feiyu Fang, Hao Lu, Xiankai |
| author_facet | Ding, Henghui Liu, Chang Wei, Yunchao Ravi, Nikhila He, Shuting Bai, Song Torr, Philip Miao, Deshui Li, Xin He, Zhenyu Wang, Yaowei Yang, Ming-Hsuan Xu, Zhensong Yao, Jiangtao Wu, Chengjing Liu, Ting Liu, Luoqi Liu, Xinyu Zhang, Jing Zhang, Kexin Yang, Yuting Jiao, Licheng Yang, Shuyuan Gao, Mingqi Luo, Jingnan Yang, Jinyu Han, Jungong Zheng, Feng Cao, Bin Zhang, Yisi Lin, Xuanxu He, Xingjian Zhao, Bo Liu, Jing Pan, Feiyu Fang, Hao Lu, Xiankai |
| contents | Pixel-level Video Understanding in the Wild Challenge (PVUW) focus on complex video understanding. In this CVPR 2024 workshop, we add two new tracks, Complex Video Object Segmentation Track based on MOSE dataset and Motion Expression guided Video Segmentation track based on MeViS dataset. In the two new tracks, we provide additional videos and annotations that feature challenging elements, such as the disappearance and reappearance of objects, inconspicuous small objects, heavy occlusions, and crowded environments in MOSE. Moreover, we provide a new motion expression guided video segmentation dataset MeViS to study the natural language-guided video understanding in complex environments. These new videos, sentences, and annotations enable us to foster the development of a more comprehensive and robust pixel-level understanding of video scenes in complex environments and realistic scenarios. The MOSE challenge had 140 registered teams in total, 65 teams participated the validation phase and 12 teams made valid submissions in the final challenge phase. The MeViS challenge had 225 registered teams in total, 50 teams participated the validation phase and 5 teams made valid submissions in the final challenge phase. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_17005 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PVUW 2024 Challenge on Complex Video Understanding: Methods and Results Ding, Henghui Liu, Chang Wei, Yunchao Ravi, Nikhila He, Shuting Bai, Song Torr, Philip Miao, Deshui Li, Xin He, Zhenyu Wang, Yaowei Yang, Ming-Hsuan Xu, Zhensong Yao, Jiangtao Wu, Chengjing Liu, Ting Liu, Luoqi Liu, Xinyu Zhang, Jing Zhang, Kexin Yang, Yuting Jiao, Licheng Yang, Shuyuan Gao, Mingqi Luo, Jingnan Yang, Jinyu Han, Jungong Zheng, Feng Cao, Bin Zhang, Yisi Lin, Xuanxu He, Xingjian Zhao, Bo Liu, Jing Pan, Feiyu Fang, Hao Lu, Xiankai Computer Vision and Pattern Recognition Pixel-level Video Understanding in the Wild Challenge (PVUW) focus on complex video understanding. In this CVPR 2024 workshop, we add two new tracks, Complex Video Object Segmentation Track based on MOSE dataset and Motion Expression guided Video Segmentation track based on MeViS dataset. In the two new tracks, we provide additional videos and annotations that feature challenging elements, such as the disappearance and reappearance of objects, inconspicuous small objects, heavy occlusions, and crowded environments in MOSE. Moreover, we provide a new motion expression guided video segmentation dataset MeViS to study the natural language-guided video understanding in complex environments. These new videos, sentences, and annotations enable us to foster the development of a more comprehensive and robust pixel-level understanding of video scenes in complex environments and realistic scenarios. The MOSE challenge had 140 registered teams in total, 65 teams participated the validation phase and 12 teams made valid submissions in the final challenge phase. The MeViS challenge had 225 registered teams in total, 50 teams participated the validation phase and 5 teams made valid submissions in the final challenge phase. |
| title | PVUW 2024 Challenge on Complex Video Understanding: Methods and Results |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.17005 |