  Recognizing attributes of objects and their parts is important to many
computer vision applications. Although great progress has been made to apply
object-level recognition, recognizing the attributes of parts remains less
applicable since the training data for part attributes recognition is usually
scarce especially for internet-scale applications. Furthermore, most existing
part attribute recognition methods rely on the part annotation which is more
expensive to obtain. To solve the data insufficiency problem and get rid of
dependence on the part annotation, we introduce a novel Concept Sharing Network
(CSN) for part attribute recognition. A great advantage of CSN is its
capability of recognizing the part attribute (a combination of part location
and appearance pattern) that has insufficient or zero training data, by
learning the part location and appearance pattern respectively from the
training data that usually mix them in a single label. Extensive experiments on
CUB-200-2011 [51], CelebA [35] and a newly proposed human attribute dataset
demonstrate the effectiveness of CSN and its advantages over other methods,
especially for the attributes with few training samples. Further experiments
show that CSN can also perform zero-shot part attribute recognition. The code
will be made available at
https://github.com/Zhaoxiangyun/Concept-Sharing-Network.
