  For online video instance segmentation (VIS), fully utilizing the information
from previous frames in an efficient manner is essential for real-time
applications. Most previous methods follow a two-stage approach requiring
additional computations such as RPN and RoIAlign, and do not fully exploit the
available information in the video for all subtasks in VIS. In this paper, we
propose a novel single-stage framework for online VIS built based on the grid
structured feature representation. The grid-based features allow us to employ
fully convolutional networks for real-time processing, and also to easily reuse
and share features within different components. We also introduce cooperatively
operating modules that aggregate information from available frames, in order to
enrich the features for all subtasks in VIS. Our design fully takes advantage
of previous information in a grid form for all tasks in VIS in an efficient
way, and we achieved the new state-of-the-art accuracy (38.6 AP and 36.9 AP)
and speed (40.0 FPS) on YouTube-VIS 2019 and 2021 datasets among online VIS
methods. The code is available at https://github.com/SuHoHan95/VISOLO.
