  Video instance segmentation (VIS) is the task that requires simultaneously
classifying, segmenting and tracking object instances of interest in video.
Recent methods typically develop sophisticated pipelines to tackle this task.
Here, we propose a new video instance segmentation framework built upon
Transformers, termed VisTR, which views the VIS task as a direct end-to-end
parallel sequence decoding/prediction problem. Given a video clip consisting of
multiple image frames as input, VisTR outputs the sequence of masks for each
instance in the video in order directly. At the core is a new, effective
instance sequence matching and segmentation strategy, which supervises and
segments instances at the sequence level as a whole. VisTR frames the instance
segmentation and tracking in the same perspective of similarity learning, thus
considerably simplifying the overall pipeline and is significantly different
from existing approaches. Without bells and whistles, VisTR achieves the
highest speed among all existing VIS models, and achieves the best result among
methods using single model on the YouTube-VIS dataset. For the first time, we
demonstrate a much simpler and faster video instance segmentation framework
built upon Transformers, achieving competitive accuracy. We hope that VisTR can
motivate future research for more video understanding tasks.
