  Recently, transformer-based image segmentation methods have achieved notable
success against previous solutions. While for video domains, how to effectively
model temporal context with the attention of object instances across frames
remains an open problem. In this paper, we propose an online video instance
segmentation framework with a novel instance-aware temporal fusion method. We
first leverages the representation, i.e., a latent code in the global context
(instance code) and CNN feature maps to represent instance- and pixel-level
features. Based on this representation, we introduce a cropping-free temporal
fusion approach to model the temporal consistency between video frames.
Specifically, we encode global instance-specific information in the instance
code and build up inter-frame contextual fusion with hybrid attentions between
the instance codes and CNN feature maps. Inter-frame consistency between the
instance codes are further enforced with order constraints. By leveraging the
learned hybrid temporal consistency, we are able to directly retrieve and
maintain instance identities across frames, eliminating the complicated
frame-wise instance matching in prior methods. Extensive experiments have been
conducted on popular VIS datasets, i.e. Youtube-VIS-19/21. Our model achieves
the best performance among all online VIS methods. Notably, our model also
eclipses all offline methods when using the ResNet-50 backbone.
