Abstract
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 imagelevel annotations, from the original class-specific
attributes. In this paper, we propose to learn imagespecific 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