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.
翻译:零样本学习旨在通过推广从已知类别中学习到的视觉特征和语义属性之间的关系来识别未知类别。最近出现的一种称为转移零样本学习的新兴范式在训练过程中进一步利用未标记的未知数据,并获得了令人印象深刻的结果。这些方法通过生成对抗网络从属性中合成未知特征以减轻针对已知类别的偏差。然而,它们忽略了未标记未知数据中的语义信息,因此无法生成高保真度的属性一致性未知特征。为了解决这个问题,我们提出了一种新的转移零样本学习方法,该方法生成未知数据的语义属性并将其强制加入到生成过程中。特别地,我们首先训练一个属性解码器,学习从视觉特征到语义属性的映射。然后,从属性解码器获得未标记数据的伪属性,并将它们整合到生成模型中,从而有助于捕捉未知类别内的细微差异,以合成更具有区分性的特征。在五个标准基准测试上的实验表明,我们的方法在零样本学习方面取得了最先进的结果。