Attribute based knowledge transfer has proven very successful in visual
object analysis and learning previously unseen classes. However, the common
approach learns and transfers attributes without taking into consideration the
embedded structure between the categories in the source set. Such information
provides important cues on the intra-attribute variations. We propose to
capture these variations in a hierarchical model that expands the knowledge
source with additional abstraction levels of attributes. We also provide a
novel transfer approach that can choose the appropriate attributes to be shared
with an unseen class. We evaluate our approach on three public datasets:
aPascal, Animals with Attributes and CUB-200-2011 Birds. The experiments
demonstrate the effectiveness of our model with significant improvement over
state-of-the-art.Comment: Published as a conference paper at WACV 2015, modifications include
new results with GoogLeNet feature
