  Attributes possess appealing properties and benefit many computer vision
problems, such as object recognition, learning with humans in the loop, and
image retrieval. Whereas the existing work mainly pursues utilizing attributes
for various computer vision problems, we contend that the most basic
problem---how to accurately and robustly detect attributes from images---has
been left under explored. Especially, the existing work rarely explicitly
tackles the need that attribute detectors should generalize well across
different categories, including those previously unseen. Noting that this is
analogous to the objective of multi-source domain generalization, if we treat
each category as a domain, we provide a novel perspective to attribute
detection and propose to gear the techniques in multi-source domain
generalization for the purpose of learning cross-category generalizable
attribute detectors. We validate our understanding and approach with extensive
experiments on four challenging datasets and three different problems.
