Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: VisQA: Quantifying Information Visualisation Recallability via Question
  Answering   Despite its importance for assessing the effectiveness of communicating
information visually, fine-grained recallability of information visualisations
has not been studied quantitatively so far. In this work we propose a visual
question answering (VQA) paradigm to study visualisation recallability and
present VisQA -- a novel VQA dataset consisting of 200 visualisations that are
annotated with crowd-sourced human (N~=~305) recallability scores obtained from
1,000 questions from five question types. Furthermore, we present the first
computational method to predict recallability of different visualisation
elements, such as the title or specific data values. We report detailed
analyses of our method on VisQA and demonstrate that it outperforms several
baselines in overall recallability and FE-, F-, RV-, and U-question
recallability. We further demonstrate one possible application of our method:
recommending the visualisation type that maximises user recallability for a
given data source. Taken together, our work makes fundamental contributions
towards a new generation of methods to assist designers in optimising
visualisations.

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The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
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The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
16
The categories of this paper are: stat
28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
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The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph
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The categories of this paper are: math-ph
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The categories of this paper are: funct-an
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The categories of this paper are: cond-mat
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The categories of this paper are: bayes-an
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The categories of this paper are: patt-sol