  In this paper, we propose a novel multi-modal framework for Scene Text Visual
Question Answering (STVQA), which requires models to read scene text in images
for question answering. Apart from text or visual objects, which could exist
independently, scene text naturally links text and visual modalities together
by conveying linguistic semantics while being a visual object in an image
simultaneously. Different to conventional STVQA models which take the
linguistic semantics and visual semantics in scene text as two separate
features, in this paper, we propose a paradigm of "Locate Then Generate" (LTG),
which explicitly unifies this two semantics with the spatial bounding box as a
bridge connecting them. Specifically, at first, LTG locates the region in an
image that may contain the answer words with an answer location module (ALM)
consisting of a region proposal network and a language refinement network, both
of which can transform to each other with one-to-one mapping via the scene text
bounding box. Next, given the answer words selected by ALM, LTG generates a
readable answer sequence with an answer generation module (AGM) based on a
pre-trained language model. As a benefit of the explicit alignment of the
visual and linguistic semantics, even without any scene text based pre-training
tasks, LTG can boost the absolute accuracy by +6.06% and +6.92% on the TextVQA
dataset and the ST-VQA dataset respectively, compared with a non-pre-training
baseline. We further demonstrate that LTG effectively unifies visual and text
modalities through the spatial bounding box connection, which is
underappreciated in previous methods.
