  Existing video instance segmentation (VIS) approaches generally follow a
closed-world assumption, where only seen category instances are identified and
spatio-temporally segmented at inference. Open-world formulation relaxes the
close-world static-learning assumption as follows: (a) first, it distinguishes
a set of known categories as well as labels an unknown object as `unknown' and
then (b) it incrementally learns the class of an unknown as and when the
corresponding semantic labels become available. We propose the first open-world
VIS approach, named OW-VISFormer, that introduces a novel feature enrichment
mechanism and a spatio-temporal objectness (STO) module. The feature enrichment
mechanism based on a light-weight auxiliary network aims at accurate
pixel-level (unknown) object delineation from the background as well as
distinguishing category-specific known semantic classes. The STO module strives
to generate instance-level pseudo-labels by enhancing the foreground
activations through a contrastive loss. Moreover, we also introduce an
extensive experimental protocol to measure the characteristics of OW-VIS. Our
OW-VISFormer performs favorably against a solid baseline in OW-VIS setting.
Further, we evaluate our contributions in the standard fully-supervised VIS
setting by integrating them into the recent SeqFormer, achieving an absolute
gain of 1.6\% AP on Youtube-VIS 2019 val. set. Lastly, we show the
generalizability of our contributions for the open-world detection (OWOD)
setting, outperforming the best existing OWOD method in the literature. Code,
models along with OW-VIS splits are available at
\url{https://github.com/OmkarThawakar/OWVISFormer}.
