Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Reducing Language Biases in Visual Question Answering with
  Visually-Grounded Question Encoder   Recent studies have shown that current VQA models are heavily biased on the
language priors in the train set to answer the question, irrespective of the
image. E.g., overwhelmingly answer "what sport is" as "tennis" or "what color
banana" as "yellow." This behavior restricts them from real-world application
scenarios. In this work, we propose a novel model-agnostic question encoder,
Visually-Grounded Question Encoder (VGQE), for VQA that reduces this effect.
VGQE utilizes both visual and language modalities equally while encoding the
question. Hence the question representation itself gets sufficient
visual-grounding, and thus reduces the dependency of the model on the language
priors. We demonstrate the effect of VGQE on three recent VQA models and
achieve state-of-the-art results on the bias-sensitive split of the VQAv2
dataset; VQA-CPv2. Further, unlike the existing bias-reduction techniques, on
the standard VQAv2 benchmark, our approach does not drop the accuracy; instead,
it improves the performance.

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The categories of this paper are: funct-an
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The categories of this paper are: alg-geom
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The categories of this paper are: nucl-th
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The categories of this paper are: atom-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: econ
5
The categories of this paper are: gr-qc
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The categories of this paper are: adap-org
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The categories of this paper are: acc-phys
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The categories of this paper are: solv-int
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-bio
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The categories of this paper are: cond-mat
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The categories of this paper are: eess
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The categories of this paper are: math-ph
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The categories of this paper are: hep-lat