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A Large-scale Attribute Dataset for Zero-shot Learning





We propose a Large-scale Attribute Dataset (LAD) which has 78,017 images of 5 super-classes, 230 classes. The image number of LAD is larger than the sum of the four most popular attribute datasets (AwA, CUB, aP/aY and SUN). 359 attributes of visual, semantic and subjective properties are defined and annotated in instance-level.
We organized an international Zero-shot Learning Competition under AI Challenger using this dataset. More than 110 teams attended the competition.

The links to download the paper, data, competition and baseline:

paper download

data download

competition link

baseline method

Contact: Bo Zhao (bozhaonanjing at Gmail)

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