  In recent years, video instance segmentation (VIS) has been largely advanced
by offline models, while online models gradually attracted less attention
possibly due to their inferior performance. However, online methods have their
inherent advantage in handling long video sequences and ongoing videos while
offline models fail due to the limit of computational resources. Therefore, it
would be highly desirable if online models can achieve comparable or even
better performance than offline models. By dissecting current online models and
offline models, we demonstrate that the main cause of the performance gap is
the error-prone association between frames caused by the similar appearance
among different instances in the feature space. Observing this, we propose an
online framework based on contrastive learning that is able to learn more
discriminative instance embeddings for association and fully exploit history
information for stability. Despite its simplicity, our method outperforms all
online and offline methods on three benchmarks. Specifically, we achieve 49.5
AP on YouTube-VIS 2019, a significant improvement of 13.2 AP and 2.1 AP over
the prior online and offline art, respectively. Moreover, we achieve 30.2 AP on
OVIS, a more challenging dataset with significant crowding and occlusions,
surpassing the prior art by 14.8 AP. The proposed method won first place in the
video instance segmentation track of the 4th Large-scale Video Object
Segmentation Challenge (CVPR2022). We hope the simplicity and effectiveness of
our method, as well as our insight into current methods, could shed light on
the exploration of VIS models.
