  In this paper, we present offline-to-online knowledge distillation (OOKD) for
video instance segmentation (VIS), which transfers a wealth of video knowledge
from an offline model to an online model for consistent prediction. Unlike
previous methods that having adopting either an online or offline model, our
single online model takes advantage of both models by distilling offline
knowledge. To transfer knowledge correctly, we propose query filtering and
association (QFA), which filters irrelevant queries to exact instances. Our KD
with QFA increases the robustness of feature matching by encoding
object-centric features from a single frame supplemented by long-range global
information. We also propose a simple data augmentation scheme for knowledge
distillation in the VIS task that fairly transfers the knowledge of all classes
into the online model. Extensive experiments show that our method significantly
improves the performance in video instance segmentation, especially for
challenging datasets including long, dynamic sequences. Our method also
achieves state-of-the-art performance on YTVIS-21, YTVIS-22, and OVIS datasets,
with mAP scores of 46.1%, 43.6%, and 31.1%, respectively.
