Zero-shot learning (ZSL) aims to recognize unseen classes by generalizing the
relation between visual features and semantic attributes learned from the seen
classes. A recent paradigm called transductive zero-shot learning further
leverages unlabeled unseen data during training and has obtained impressive
results. These methods always synthesize unseen features from attributes
through a generative adversarial network to mitigate the bias towards seen
classes. However, they neglect the semantic information in the unlabeled unseen
data and thus fail to generate high-fidelity attribute-consistent unseen
features. To address this issue, we present a novel transductive ZSL method
that produces semantic attributes of the unseen data and imposes them on the
generative process. In particular, we first train an attribute decoder that
learns the mapping from visual features to semantic attributes. Then, from the
attribute decoder, we obtain pseudo-attributes of unlabeled data and integrate
them into the generative model, which helps capture the detailed differences
within unseen classes so as to synthesize more discriminative features.
Experiments on five standard benchmarks show that our method yields
state-of-the-art results for zero-shot learning.

本文提出一种新的传输零样本学习方法，该方法使用生成对抗网络从未见过的特征提取出语义属性，并将其融合到产生模型中，从而捕获未见类别内的细微差异，合成更具辨别能力的特征。在五个标准基准测试中，本方法取得了零样本学习的最新成果。

利用语义属性进行归纳零样本学习

Exploiting Semantic Attributes for Transductive Zero-Shot Learning

Zero-shot learning, the task of learning to recognize new classes not seen
during training, has received considerable attention in the case of 2D image
classification. However despite the increasing ubiquity of 3D sensors, the
corresponding 3D point cloud classification problem has not been meaningfully
explored and introduces new challenges. This paper extends, for the first time,
transductive Zero-Shot Learning (ZSL) and Generalized Zero-Shot Learning (GZSL)
approaches to the domain of 3D point cloud classification. To this end, a novel
triplet loss is developed that takes advantage of unlabeled test data. While
designed for the task of 3D point cloud classification, the method is also
shown to be applicable to the more common use-case of 2D image classification.
An extensive set of experiments is carried out, establishing state-of-the-art
for ZSL and GZSL in the 3D point cloud domain, as well as demonstrating the
applicability of the approach to the image domain.

本文将零样本学习扩展到三维点云分类领域，提出基于三元组损失的方法，并在实验中证明了其在三维点云和二维图像分类任务中的有效性。