The objective of Classic Referring Expression Comprehension (REC) is to
produce a bounding box corresponding to the object mentioned in a given textual
description. Commonly, existing datasets and techniques in classic REC are
tailored for expressions that pertain to a single target, meaning a sole
expression is linked to one specific object. Expressions that refer to multiple
targets or involve no specific target have not been taken into account. This
constraint hinders the practical applicability of REC. This study introduces a
new benchmark termed as Generalized Referring Expression Comprehension (GREC).
This benchmark extends the classic REC by permitting expressions to describe
any number of target objects. To achieve this goal, we have built the first
large-scale GREC dataset named gRefCOCO. This dataset encompasses a range of
expressions: those referring to multiple targets, expressions with no specific
target, and the single-target expressions. The design of GREC and gRefCOCO
ensures smooth compatibility with classic REC. The proposed gRefCOCO dataset, a
GREC method implementation code, and GREC evaluation code are available at
this https URL

通过引入被称为 gRefCOCO 的新基准，这项研究扩展了经典的推理表达理解（REC），允许表达描述任意数量的目标对象，并建立了第一个大规模的 GREC 数据集。