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.

本文提出一种新方法，通过将原始视觉特征投影到一个新的（无冗余）特征空间并限制这两个特征空间之间的统计依赖来学习通用零样本学习的无冗余特征，以减少细粒度对象中的多余信息，并在不失去区分信息的情况下保持分类关系，实验结果表明，与现有技术相比，我们的 RFF-GZSL 方法在四个基准数据集上取得了竞争性结果。