Attribute Learning in Large-Scale Datasets

Russakovsky, Olga; Fei-Fei, Li

doi:10.1007/978-3-642-35749-7_1

Attribute Learning in Large-Scale Datasets

Olga Russakovsky, Li Fei-Fei

ECCVW 2010 pp. 1-14

doi:10.1007/978-3-642-35749-7_1 /eccvw/2010/russakovsky2010eccvw-attribute/

Abstract

We consider the task of learning visual connections between object categories using the ImageNet dataset, which is a large-scale dataset ontology containing more than 15 thousand object classes. We want to discover visual relationships between the classes that are currently missing (such as similar colors or shapes or textures). In this work we learn 20 visual attributes and use them in a zero-shot transfer learning experiment as well as to make visual connections between semantically unrelated object categories.

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Cite

Text

Russakovsky and Fei-Fei. "Attribute Learning in Large-Scale Datasets." European Conference on Computer Vision Workshops, 2010. doi:10.1007/978-3-642-35749-7_1

Markdown

[Russakovsky and Fei-Fei. "Attribute Learning in Large-Scale Datasets." European Conference on Computer Vision Workshops, 2010.](https://mlanthology.org/eccvw/2010/russakovsky2010eccvw-attribute/) doi:10.1007/978-3-642-35749-7_1

BibTeX

@inproceedings{russakovsky2010eccvw-attribute,
  title     = {{Attribute Learning in Large-Scale Datasets}},
  author    = {Russakovsky, Olga and Fei-Fei, Li},
  booktitle = {European Conference on Computer Vision Workshops},
  year      = {2010},
  pages     = {1-14},
  doi       = {10.1007/978-3-642-35749-7_1},
  url       = {https://mlanthology.org/eccvw/2010/russakovsky2010eccvw-attribute/}
}