Recognizing Part Attributes with Insufficient Data

Xiangyun Zhao, Yi Yang, Feng Zhou, Xiao Tan, Yuchen Yuan, Yingze Bao, Ying Wu

Abstract:

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.