  Recognising objects according to a pre-defined fixed set of class labels has
been well studied in the Computer Vision. There are a great many practical
applications where the subjects that may be of interest are not known
beforehand, or so easily delineated, however. In many of these cases natural
language dialog is a natural way to specify the subject of interest, and the
task achieving this capability (a.k.a, Referring Expression Comprehension) has
recently attracted attention. To this end we propose a unified framework, the
ParalleL AttentioN (PLAN) network, to discover the object in an image that is
being referred to in variable length natural expression descriptions, from
short phrases query to long multi-round dialogs. The PLAN network has two
attention mechanisms that relate parts of the expressions to both the global
visual content and also directly to object candidates. Furthermore, the
attention mechanisms are recurrent, making the referring process visualizable
and explainable. The attended information from these dual sources are combined
to reason about the referred object. These two attention mechanisms can be
trained in parallel and we find the combined system outperforms the
state-of-art on several benchmarked datasets with different length language
input, such as RefCOCO, RefCOCO+ and GuessWhat?!.
