Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Characterizing Datasets for Social Visual Question Answering, and the
  New TinySocial Dataset   Modern social intelligence includes the ability to watch videos and answer
questions about social and theory-of-mind-related content, e.g., for a scene in
Harry Potter, "Is the father really upset about the boys flying the car?"
Social visual question answering (social VQA) is emerging as a valuable
methodology for studying social reasoning in both humans (e.g., children with
autism) and AI agents. However, this problem space spans enormous variations in
both videos and questions. We discuss methods for creating and characterizing
social VQA datasets, including 1) crowdsourcing versus in-house authoring,
including sample comparisons of two new datasets that we created
(TinySocial-Crowd and TinySocial-InHouse) and the previously existing Social-IQ
dataset; 2) a new rubric for characterizing the difficulty and content of a
given video; and 3) a new rubric for characterizing question types. We close by
describing how having well-characterized social VQA datasets will enhance the
explainability of AI agents and can also inform assessments and educational
interventions for people.

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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: dg-ga
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The categories of this paper are: math
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The categories of this paper are: chao-dyn
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The categories of this paper are: comp-gas
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The categories of this paper are: cmp-lg
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The categories of this paper are: physics
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The categories of this paper are: funct-an
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The categories of this paper are: gr-qc
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The categories of this paper are: stat
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The categories of this paper are: hep-th
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The categories of this paper are: q-alg
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The categories of this paper are: mtrl-th
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The categories of this paper are: atom-ph
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The categories of this paper are: math-ph