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Recognising objects according to a pre-defined fixed set of class labels hasbeen well studied in the Computer Vision. There are a great many practicalapplications where the subjects that may be of interest are not knownbeforehand, or so easily delineated, however. In many of these cases naturallanguage dialog is a natural way to specify the subject of interest, and thetask achieving this capability (a.k.a, Referring Expression Comprehension) hasrecently attracted attention. To this end we propose a unified framework, theParalleL AttentioN (PLAN) network, to discover the object in an image that isbeing referred to in variable length natural expression descriptions, fromshort phrases query to long multi-round dialogs. The PLAN network has twoattention mechanisms that relate parts of the expressions to both the globalvisual content and also directly to object candidates. Furthermore, theattention mechanisms are recurrent, making the referring process visualizableand explainable. The attended information from these dual sources are combinedto reason about the referred object. These two attention mechanisms can betrained in parallel and we find the combined system outperforms thestate-of-art on several benchmarked datasets with different length languageinput, such as RefCOCO, RefCOCO+ and GuessWhat?!.

Parallel Attention: A Unified Framework for Visual Object Discovery through Dialogs and Queries