  Referring expression comprehension (REC) aims to localize a target object in
an image described by a referring expression phrased in natural language.
Different from the object detection task that queried object labels have been
pre-defined, the REC problem only can observe the queries during the test. It
thus more challenging than a conventional computer vision problem. This task
has attracted a lot of attention from both computer vision and natural language
processing community, and several lines of work have been proposed, from
CNN-RNN model, modular network to complex graph-based model. In this survey, we
first examine the state of the art by comparing modern approaches to the
problem. We classify methods by their mechanism to encode the visual and
textual modalities. In particular, we examine the common approach of joint
embedding images and expressions to a common feature space. We also discuss
modular architectures and graph-based models that interface with structured
graph representation. In the second part of this survey, we review the datasets
available for training and evaluating REC systems. We then group results
according to the datasets, backbone models, settings so that they can be fairly
compared. Finally, we discuss promising future directions for the field, in
particular the compositional referring expression comprehension that requires
longer reasoning chain to address.
