  In this paper, we are tackling the weakly-supervised referring expression
grounding task, for the localization of a referent object in an image according
to a query sentence, where the mapping between image regions and queries are
not available during the training stage. In traditional methods, an object
region that best matches the referring expression is picked out, and then the
query sentence is reconstructed from the selected region, where the
reconstruction difference serves as the loss for back-propagation. The existing
methods, however, conduct both the matching and the reconstruction
approximately as they ignore the fact that the matching correctness is unknown.
To overcome this limitation, a discriminative triad is designed here as the
basis to the solution, through which a query can be converted into one or
multiple discriminative triads in a very scalable way. Based on the
discriminative triad, we further propose the triad-level matching and
reconstruction modules which are lightweight yet effective for the
weakly-supervised training, making it three times lighter and faster than the
previous state-of-the-art methods. One important merit of our work is its
superior performance despite the simple and neat design. Specifically, the
proposed method achieves a new state-of-the-art accuracy when evaluated on
RefCOCO (39.21%), RefCOCO+ (39.18%) and RefCOCOg (43.24%) datasets, that is
4.17%, 4.08% and 7.8% higher than the previous one, respectively.
