  Referring Expression Segmentation (RES) aims to generate a segmentation mask
for the object described by a given language expression. Existing classic RES
datasets and methods commonly support single-target expressions only, i.e., one
expression refers to one target object. Multi-target and no-target expressions
are not considered. This limits the usage of RES in practice. In this paper, we
introduce a new benchmark called Generalized Referring Expression Segmentation
(GRES), which extends the classic RES to allow expressions to refer to an
arbitrary number of target objects. Towards this, we construct the first
large-scale GRES dataset called gRefCOCO that contains multi-target, no-target,
and single-target expressions. GRES and gRefCOCO are designed to be
well-compatible with RES, facilitating extensive experiments to study the
performance gap of the existing RES methods on the GRES task. In the
experimental study, we find that one of the big challenges of GRES is complex
relationship modeling. Based on this, we propose a region-based GRES baseline
ReLA that adaptively divides the image into regions with sub-instance clues,
and explicitly models the region-region and region-language dependencies. The
proposed approach ReLA achieves new state-of-the-art performance on the both
newly proposed GRES and classic RES tasks. The proposed gRefCOCO dataset and
method are available at https://henghuiding.github.io/GRES.
