  The handling of long videos with complex and occluded sequences has recently
emerged as a new challenge in the video instance segmentation (VIS) community.
However, existing methods have limitations in addressing this challenge. We
argue that the biggest bottleneck in current approaches is the discrepancy
between training and inference. To effectively bridge this gap, we propose a
Generalized framework for VIS, namely GenVIS, that achieves state-of-the-art
performance on challenging benchmarks without designing complicated
architectures or requiring extra post-processing. The key contribution of
GenVIS is the learning strategy, which includes a query-based training pipeline
for sequential learning with a novel target label assignment. Additionally, we
introduce a memory that effectively acquires information from previous states.
Thanks to the new perspective, which focuses on building relationships between
separate frames or clips, GenVIS can be flexibly executed in both online and
semi-online manner. We evaluate our approach on popular VIS benchmarks,
achieving state-of-the-art results on YouTube-VIS 2019/2021/2022 and Occluded
VIS (OVIS). Notably, we greatly outperform the state-of-the-art on the long VIS
benchmark (OVIS), improving 5.6 AP with ResNet-50 backbone. Code is available
at https://github.com/miranheo/GenVIS.
