  We focus on the confounding bias between language and location in the visual
grounding pipeline, where we find that the bias is the major visual reasoning
bottleneck. For example, the grounding process is usually a trivial
language-location association without visual reasoning, e.g., grounding any
language query containing sheep to the nearly central regions, due to that most
queries about sheep have ground-truth locations at the image center. First, we
frame the visual grounding pipeline into a causal graph, which shows the
causalities among image, query, target location and underlying confounder.
Through the causal graph, we know how to break the grounding bottleneck:
deconfounded visual grounding. Second, to tackle the challenge that the
confounder is unobserved in general, we propose a confounder-agnostic approach
called: Referring Expression Deconfounder (RED), to remove the confounding
bias. Third, we implement RED as a simple language attention, which can be
applied in any grounding method. On popular benchmarks, RED improves various
state-of-the-art grounding methods by a significant margin. Code will soon be
available at: https://github.com/JianqiangH/Deconfounded_VG.
