Video instance segmentation aims to detect, segment, and track objects in a
video. Current approaches extend image-level segmentation algorithms to the
temporal domain. However, this results in temporally inconsistent masks. In
this work, we identify the mask quality due to temporal stability as a
performance bottleneck. Motivated by this, we propose a video instance
segmentation method that alleviates the problem due to missing detections.
Since this cannot be solved simply using spatial information, we leverage
temporal context using inter-frame attentions. This allows our network to
refocus on missing objects using box predictions from the neighbouring frame,
thereby overcoming missing detections. Our method significantly outperforms
previous state-of-the-art algorithms using the Mask R-CNN backbone, by
achieving 36.0% mAP on the YouTube-VIS benchmark. Additionally, our method is
completely online and requires no future frames. Our code is publicly available
at this https URL

该论文提出了一种通过利用邻帧预测的空间信息并结合时间域中的帧间注意力机制来提高视频实体分割质量的方法，其在 YouTube-VIS 数据集上取得 36.0% 的 mAP，且该方法是完全在线的，不需要使用未来的视频帧。