  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 .
