  We propose MinVIS, a minimal video instance segmentation (VIS) framework that
achieves state-of-the-art VIS performance with neither video-based
architectures nor training procedures. By only training a query-based image
instance segmentation model, MinVIS outperforms the previous best result on the
challenging Occluded VIS dataset by over 10% AP. Since MinVIS treats frames in
training videos as independent images, we can drastically sub-sample the
annotated frames in training videos without any modifications. With only 1% of
labeled frames, MinVIS outperforms or is comparable to fully-supervised
state-of-the-art approaches on YouTube-VIS 2019/2021. Our key observation is
that queries trained to be discriminative between intra-frame object instances
are temporally consistent and can be used to track instances without any
manually designed heuristics. MinVIS thus has the following inference pipeline:
we first apply the trained query-based image instance segmentation to video
frames independently. The segmented instances are then tracked by bipartite
matching of the corresponding queries. This inference is done in an online
fashion and does not need to process the whole video at once. MinVIS thus has
the practical advantages of reducing both the labeling costs and the memory
requirements, while not sacrificing the VIS performance. Code is available at:
https://github.com/NVlabs/MinVIS
