Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Localize, Group, and Select: Boosting Text-VQA by Scene Text Modeling   As an important task in multimodal context understanding, Text-VQA (Visual
Question Answering) aims at question answering through reading text information
in images. It differentiates from the original VQA task as Text-VQA requires
large amounts of scene-text relationship understanding, in addition to the
cross-modal grounding capability. In this paper, we propose Localize, Group,
and Select (LOGOS), a novel model which attempts to tackle this problem from
multiple aspects. LOGOS leverages two grounding tasks to better localize the
key information of the image, utilizes scene text clustering to group
individual OCR tokens, and learns to select the best answer from different
sources of OCR (Optical Character Recognition) texts. Experiments show that
LOGOS outperforms previous state-of-the-art methods on two Text-VQA benchmarks
without using additional OCR annotation data. Ablation studies and analysis
demonstrate the capability of LOGOS to bridge different modalities and better
understand scene text.

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The categories of this paper are: chao-dyn
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The categories of this paper are: math-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-ex
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The categories of this paper are: patt-sol
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The categories of this paper are: comp-gas
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The categories of this paper are: adap-org
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The categories of this paper are: atom-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: math
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The categories of this paper are: hep-ph
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The categories of this paper are: econ
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The categories of this paper are: solv-int
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The categories of this paper are: alg-geom
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The categories of this paper are: nucl-th