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. © 2012 Springer-Verlag.
Cite
CITATION STYLE
Russakovsky, O., & Fei-Fei, L. (2012). Attribute learning in large-scale datasets. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6553 LNCS, pp. 1–14). https://doi.org/10.1007/978-3-642-35749-7_1
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