Can our video understanding systems perceive objects when a heavy occlusion
exists in a scene?
To answer this question, we collect a large-scale dataset called OVIS for
occluded video instance segmentation, that is, to simultaneously detect,
segment, and track instances in occluded scenes. OVIS consists of 296k
high-quality instance masks from 25 semantic categories, where object
occlusions usually occur. While our human vision systems can understand those
occluded instances by contextual reasoning and association, our experiments
suggest that current video understanding systems cannot. On the OVIS dataset,
the highest AP achieved by state-of-the-art algorithms is only 16.3, which
reveals that we are still at a nascent stage for understanding objects,
instances, and videos in a real-world scenario. We also present a simple
plug-and-play module that performs temporal feature calibration to complement
missing object cues caused by occlusion. Built upon MaskTrack R-CNN and
SipMask, we obtain a remarkable AP improvement on the OVIS dataset. The OVIS
dataset and project code are available at this http URL .

我们收集了一个大规模的被遮挡的视频实例分割数据集，即检测，在遮挡场景中同时分割和跟踪实例，实验表明，现有的视频理解系统无法理解这些被遮挡的实例，我们还提出了一个简单的插件模块，用于补充遮挡引起的缺失对象线索，建立在 MaskTrack R-CNN 和 SipMask 上，在 OVIS 数据集上取得了显着的 AP 提升。