Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Prediction of Search Targets From Fixations in Open-World Settings   Previous work on predicting the target of visual search from human fixations
only considered closed-world settings in which training labels are available
and predictions are performed for a known set of potential targets. In this
work we go beyond the state of the art by studying search target prediction in
an open-world setting in which we no longer assume that we have fixation data
to train for the search targets. We present a dataset containing fixation data
of 18 users searching for natural images from three image categories within
synthesised image collages of about 80 images. In a closed-world baseline
experiment we show that we can predict the correct target image out of a
candidate set of five images. We then present a new problem formulation for
search target prediction in the open-world setting that is based on learning
compatibilities between fixations and potential targets.

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The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
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The categories of this paper are: supr-con
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The categories of this paper are: funct-an
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The categories of this paper are: hep-lat
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-fin
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The categories of this paper are: bayes-an
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The categories of this paper are: q-bio
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The categories of this paper are: cmp-lg
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The categories of this paper are: atom-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: ao-sci
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The categories of this paper are: nucl-th
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The categories of this paper are: quant-ph