  Spatial Description Resolution, as a language-guided localization task, is
proposed for target location in a panoramic street view, given corresponding
language descriptions. Explicitly characterizing an object-level relationship
while distilling spatial relationships are currently absent but crucial to this
task. Mimicking humans, who sequentially traverse spatial relationship words
and objects with a first-person view to locate their target, we propose a novel
spatial relationship induced (SIRI) network. Specifically, visual features are
firstly correlated at an implicit object-level in a projected latent space;
then they are distilled by each spatial relationship word, resulting in each
differently activated feature representing each spatial relationship. Further,
we introduce global position priors to fix the absence of positional
information, which may result in global positional reasoning ambiguities. Both
the linguistic and visual features are concatenated to finalize the target
localization. Experimental results on the Touchdown show that our method is
around 24\% better than the state-of-the-art method in terms of accuracy,
measured by an 80-pixel radius. Our method also generalizes well on our
proposed extended dataset collected using the same settings as Touchdown.
