  Open-world instance segmentation is the task of grouping pixels into object
instances without any pre-determined taxonomy. This is challenging, as
state-of-the-art methods rely on explicit class semantics obtained from large
labeled datasets, and out-of-domain evaluation performance drops significantly.
Here we propose a novel approach for mask proposals, Generic Grouping Networks
(GGNs), constructed without semantic supervision. Our approach combines a local
measure of pixel affinity with instance-level mask supervision, producing a
training regimen designed to make the model as generic as the data diversity
allows. We introduce a method for predicting Pairwise Affinities (PA), a
learned local relationship between pairs of pixels. PA generalizes very well to
unseen categories. From PA we construct a large set of pseudo-ground-truth
instance masks; combined with human-annotated instance masks we train GGNs and
significantly outperform the SOTA on open-world instance segmentation on
various benchmarks including COCO, LVIS, ADE20K, and UVO. Code is available on
project website: https://sites.google.com/view/generic-grouping/.
