Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The 2021 Image Similarity Dataset and Challenge   This paper introduces a new benchmark for large-scale image similarity
detection. This benchmark is used for the Image Similarity Challenge at
NeurIPS'21 (ISC2021). The goal is to determine whether a query image is a
modified copy of any image in a reference corpus of size 1~million. The
benchmark features a variety of image transformations such as automated
transformations, hand-crafted image edits and machine-learning based
manipulations. This mimics real-life cases appearing in social media, for
example for integrity-related problems dealing with misinformation and
objectionable content. The strength of the image manipulations, and therefore
the difficulty of the benchmark, is calibrated according to the performance of
a set of baseline approaches. Both the query and reference set contain a
majority of "distractor" images that do not match, which corresponds to a
real-life needle-in-haystack setting, and the evaluation metric reflects that.
We expect the DISC21 benchmark to promote image copy detection as an important
and challenging computer vision task and refresh the state of the art. Code and
data are available at https://github.com/facebookresearch/isc2021

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The categories of this paper are: stat
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The categories of this paper are: q-bio
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: chao-dyn
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The categories of this paper are: funct-an
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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: cond-mat
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: quant-ph
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The categories of this paper are: q-fin
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The categories of this paper are: alg-geom