To learn semantic attributes, existing methods typically train one
discriminative model for each word in a vocabulary of nameable properties.
However, this "one model per word" assumption is problematic: while a word
might have a precise linguistic definition, it need not have a precise visual
definition. We propose to discover shades of attribute meaning. Given an
attribute name, we use crowdsourced image labels to discover the latent factors
underlying how different annotators perceive the named concept. We show that
structure in those latent factors helps reveal shades, that is, interpretations
for the attribute shared by some group of annotators. Using these shades, we
train classifiers to capture the primary (often subtle) variants of the
attribute. The resulting models are both semantic and visually precise. By
catering to users' interpretations, they improve attribute prediction accuracy
on novel images. Shades also enable more successful attribute-based image
search, by providing robust personalized models for retrieving multi-attribute
query results. They are widely applicable to tasks that involve describing
visual content, such as zero-shot category learning and organization of photo
collections.

该研究提出一种使用众包图像标签发现属性名称的潜在因素的方法，以捕捉属性的主要变体，从而提高基于属性的图像搜索的精度，以及用于描述视觉内容的一般任务中的广泛应用。