Occluded Video Instance Segmentation: Dataset and ICCV 2021 Challenge
Publikation: Working paper › Preprint › Forskning
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Occluded Video Instance Segmentation: Dataset and ICCV 2021 Challenge. / Belongie, Serge; Qi, Jiyang; Gao, Yan; Wang, Xinggang; Liu, Xiaoyu; Bai, Xiang; Yuille, Alan; Torr, Philip H. S.; Bai, Song.
2021.Publikation: Working paper › Preprint › Forskning
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TY - UNPB
T1 - Occluded Video Instance Segmentation: Dataset and ICCV 2021 Challenge
AU - Belongie, Serge
AU - Qi, Jiyang
AU - Gao, Yan
AU - Wang, Xinggang
AU - Liu, Xiaoyu
AU - Bai, Xiang
AU - Yuille, Alan
AU - Torr, Philip H. S.
AU - Bai, Song
PY - 2021/11/15
Y1 - 2021/11/15
N2 - Although deep learning methods have achieved advanced video object recognition performance in recent years, perceiving heavily occluded objects in a video is still a very challenging task. To promote the development of occlusion understanding, we collect a large-scale dataset called OVIS for video instance segmentation in the occluded scenario. OVIS consists of 296k high-quality instance masks and 901 occluded scenes. While our human vision systems can perceive those occluded objects by contextual reasoning and association, our experiments suggest that current video understanding systems cannot. On the OVIS dataset, all baseline methods encounter a significant performance degradation of about 80% in the heavily occluded object group, which demonstrates that there is still a long way to go in understanding obscured objects and videos in a complex real-world scenario. To facilitate the research on new paradigms for video understanding systems, we launched a challenge based on the OVIS dataset. The submitted top-performing algorithms have achieved much higher performance than our baselines. In this paper, we will introduce the OVIS dataset and further dissect it by analyzing the results of baselines and submitted methods. The OVIS dataset and challenge information can be found at http://songbai.site/ovis .
AB - Although deep learning methods have achieved advanced video object recognition performance in recent years, perceiving heavily occluded objects in a video is still a very challenging task. To promote the development of occlusion understanding, we collect a large-scale dataset called OVIS for video instance segmentation in the occluded scenario. OVIS consists of 296k high-quality instance masks and 901 occluded scenes. While our human vision systems can perceive those occluded objects by contextual reasoning and association, our experiments suggest that current video understanding systems cannot. On the OVIS dataset, all baseline methods encounter a significant performance degradation of about 80% in the heavily occluded object group, which demonstrates that there is still a long way to go in understanding obscured objects and videos in a complex real-world scenario. To facilitate the research on new paradigms for video understanding systems, we launched a challenge based on the OVIS dataset. The submitted top-performing algorithms have achieved much higher performance than our baselines. In this paper, we will introduce the OVIS dataset and further dissect it by analyzing the results of baselines and submitted methods. The OVIS dataset and challenge information can be found at http://songbai.site/ovis .
UR - https://arxiv.org/abs/2111.07950
M3 - Preprint
BT - Occluded Video Instance Segmentation: Dataset and ICCV 2021 Challenge
ER -
ID: 303770464