  Video instance segmentation aims at predicting object segmentation masks for
each frame, as well as associating the instances across multiple frames. Recent
end-to-end video instance segmentation methods are capable of performing object
segmentation and instance association together in a direct parallel sequence
decoding/prediction framework. Although these methods generally predict higher
quality object segmentation masks, they can fail to associate instances in
challenging cases because they do not explicitly model the temporal instance
consistency for adjacent frames. We propose a consistent end-to-end video
instance segmentation framework with Inter-Frame Recurrent Attention to model
both the temporal instance consistency for adjacent frames and the global
temporal context. Our extensive experiments demonstrate that the Inter-Frame
Recurrent Attention significantly improves temporal instance consistency while
maintaining the quality of the object segmentation masks. Our model achieves
state-of-the-art accuracy on both YouTubeVIS-2019 (62.1\%) and YouTubeVIS-2021
(54.7\%) datasets. In addition, quantitative and qualitative results show that
the proposed methods predict more temporally consistent instance segmentation
masks.
