Abstract
In this paper, we address an open problem of zero-shot
learning. Its principle is based on learning a mapping
that associates feature vectors extracted from i.e. images
and attribute vectors that describe objects and/or scenes
of interest. In turns, this allows classifying unseen object
classes and/or scenes by matching feature vectors via mapping to a newly defined attribute vector describing a new
class. Due to importance of such a learning task, there exist many methods that learn semantic, probabilistic, linear
or piece-wise linear mappings. In contrast, we apply wellestablished kernel methods to learn a non-linear mapping
between the feature and attribute spaces. We propose an
easy learning objective inspired by the Linear Discriminant
Analysis, Kernel-Target Alignment and Kernel Polarization
methods [12, 8, 4] that promotes incoherence. We evaluate the performance of our algorithm on the Polynomial as
well as shift-invariant Gaussian and Cauchy kernels. Despite simplicity of our approach, we obtain state-of-the-art
results on several zero-shot learning datasets and benchmarks including a recent AWA2 dataset