  As a kind of semantic representation of visual object descriptions,
attributes are widely used in various computer vision tasks. In most of
existing attribute-based research, class-specific attributes (CSA), which are
class-level annotations, are usually adopted due to its low annotation cost for
each class instead of each individual image. However, class-specific attributes
are usually noisy because of annotation errors and diversity of individual
images. Therefore, it is desirable to obtain image-specific attributes (ISA),
which are image-level annotations, from the original class-specific attributes.
In this paper, we propose to learn image-specific attributes by graph-based
attribute propagation. Considering the intrinsic property of hyperbolic
geometry that its distance expands exponentially, hyperbolic neighborhood graph
(HNG) is constructed to characterize the relationship between samples. Based on
HNG, we define neighborhood consistency for each sample to identify
inconsistent samples. Subsequently, inconsistent samples are refined based on
their neighbors in HNG. Extensive experiments on five benchmark datasets
demonstrate the significant superiority of the learned image-specific
attributes over the original class-specific attributes in the zero-shot object
classification task.
