  Video instance segmentation (VIS) is a critical task with diverse
applications, including autonomous driving and video editing. Existing methods
often underperform on complex and long videos in real world, primarily due to
two factors. Firstly, offline methods are limited by the tightly-coupled
modeling paradigm, which treats all frames equally and disregards the
interdependencies between adjacent frames. Consequently, this leads to the
introduction of excessive noise during long-term temporal alignment. Secondly,
online methods suffer from inadequate utilization of temporal information. To
tackle these challenges, we propose a decoupling strategy for VIS by dividing
it into three independent sub-tasks: segmentation, tracking, and refinement.
The efficacy of the decoupling strategy relies on two crucial elements: 1)
attaining precise long-term alignment outcomes via frame-by-frame association
during tracking, and 2) the effective utilization of temporal information
predicated on the aforementioned accurate alignment outcomes during refinement.
We introduce a novel referring tracker and temporal refiner to construct the
\textbf{D}ecoupled \textbf{VIS} framework (\textbf{DVIS}). DVIS achieves new
SOTA performance in both VIS and VPS, surpassing the current SOTA methods by
7.3 AP and 9.6 VPQ on the OVIS and VIPSeg datasets, which are the most
challenging and realistic benchmarks. Moreover, thanks to the decoupling
strategy, the referring tracker and temporal refiner are super light-weight
(only 1.69\% of the segmenter FLOPs), allowing for efficient training and
inference on a single GPU with 11G memory. The code is available at
\href{https://github.com/zhang-tao-whu/DVIS}{https://github.com/zhang-tao-whu/DVIS}.
