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The 2021 Image Similarity Dataset and Challenge

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arxiv 2106.09672 v4 pith:LJ537DW2 submitted 2021-06-17 cs.CV

The 2021 Image Similarity Dataset and Challenge

Matthijs Douze , Giorgos Tolias , Ed Pizzi , Zo\"e Papakipos , Lowik Chanussot , Filip Radenovic , Tomas Jenicek , Maxim Maximov
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Laura Leal-Taix\'e Ismail Elezi Ond\v{r}ej Chum Cristian Canton Ferrer
This is my paper
classification cs.CV
keywords imagebenchmarksimilaritychallengecopydetectionisc2021manipulations
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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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