Occluded Video Instance Segmentation: Dataset and ICCV 2021 Challenge

Understand

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.