Icon645 is a large-scale dataset of icon images that cover a wide range of objects:

  • 645,687 colored icons
  • 377 different icon classes

These collected icon classes are frequently mentioned in the IconQA questions. In this work, we use the icon data to pre-train backbone networks on the icon classification task in order to extract semantic representations from abstract diagrams in IconQA. On top of pre-training encoders, the large-scale icon data could also contribute to open research on abstract aesthetics and symbolic visual understanding.


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