  Exploring dense matching between the current frame and past frames for
long-range context modeling, memory-based methods have demonstrated impressive
results in video object segmentation (VOS) recently. Nevertheless, due to the
lack of instance understanding ability, the above approaches are oftentimes
brittle to large appearance variations or viewpoint changes resulted from the
movement of objects and cameras. In this paper, we argue that instance
understanding matters in VOS, and integrating it with memory-based matching can
enjoy the synergy, which is intuitively sensible from the definition of VOS
task, \ie, identifying and segmenting object instances within the video.
Towards this goal, we present a two-branch network for VOS, where the
query-based instance segmentation (IS) branch delves into the instance details
of the current frame and the VOS branch performs spatial-temporal matching with
the memory bank. We employ the well-learned object queries from IS branch to
inject instance-specific information into the query key, with which the
instance-augmented matching is further performed. In addition, we introduce a
multi-path fusion block to effectively combine the memory readout with
multi-scale features from the instance segmentation decoder, which incorporates
high-resolution instance-aware features to produce final segmentation results.
Our method achieves state-of-the-art performance on DAVIS 2016/2017 val (92.6%
and 87.1%), DAVIS 2017 test-dev (82.8%), and YouTube-VOS 2018/2019 val (86.3%
and 86.3%), outperforming alternative methods by clear margins.
