  Zero-Shot Learning (ZSL) has attracted huge research attention over the past
few years; it aims to learn the new concepts that have never been seen before.
In classical ZSL algorithms, attributes are introduced as the intermediate
semantic representation to realize the knowledge transfer from seen classes to
unseen classes. Previous ZSL algorithms are tested on several benchmark
datasets annotated with attributes. However, these datasets are defective in
terms of the image distribution and attribute diversity. In addition, we argue
that the "co-occurrence bias problem" of existing datasets, which is caused by
the biased co-occurrence of objects, significantly hinders models from
correctly learning the concept. To overcome these problems, we propose a
Large-scale Attribute Dataset (LAD). Our dataset 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. 359 attributes of visual, semantic
and subjective properties are defined and annotated in instance-level. We
analyze our dataset by conducting both supervised learning and zero-shot
learning tasks. Seven state-of-the-art ZSL algorithms are tested on this new
dataset. The experimental results reveal the challenge of implementing
zero-shot learning on our dataset.
