Recently, query based deep networks catch lots of attention owing to their
end-to-end pipeline and competitive results on several fundamental computer
vision tasks, such as object detection, semantic segmentation, and instance
segmentation. However, how to establish a query based video instance
segmentation (VIS) framework with elegant architecture and strong performance
remains to be settled. In this paper, we present \textbf{QueryTrack} (i.e.,
tracking instances as queries), a unified query based VIS framework fully
leveraging the intrinsic one-to-one correspondence between instances and
queries in QueryInst. The proposed method obtains 52.7 / 52.3 AP on
YouTube-VIS-2019 / 2021 datasets, which wins the 2-nd place in the YouTube-VIS
Challenge at CVPR 2021 \textbf{with a single online end-to-end model, single
scale testing \& modest amount of training data}. We also provide
QueryTrack-ResNet-50 baseline results on YouTube-VIS-2021 val set as references
for the VIS community.

本文提出了一种名为 QueryTrack 的统一查询 VIS 框架，该框架利用 QueryInst 中实例和查询之间固有的一对一对应关系，采用端到端模型、单尺度测试和适量的训练数据，在 YouTube-VIS-2019/2021 数据集上实现了 52.7/52.3 AP，并在 CVPR 2021 的 YouTube-VIS 挑战赛中获得第二名。