  Zero-shot object recognition or zero-shot learning aims to transfer the
object recognition ability among the semantically related categories, such as
fine-grained animal or bird species. However, the images of different
fine-grained objects tend to merely exhibit subtle differences in appearance,
which will severely deteriorate zero-shot object recognition. To reduce the
superfluous information in the fine-grained objects, in this paper, we propose
to learn the redundancy-free features for generalized zero-shot learning. We
achieve our motivation by projecting the original visual features into a new
(redundancy-free) feature space and then restricting the statistical dependence
between these two feature spaces. Furthermore, we require the projected
features to keep and even strengthen the category relationship in the
redundancy-free feature space. In this way, we can remove the redundant
information from the visual features without losing the discriminative
information. We extensively evaluate the performance on four benchmark
datasets. The results show that our redundancy-free feature based generalized
zero-shot learning (RFF-GZSL) approach can achieve competitive results compared
with the state-of-the-arts.
