零样本分类的目标是对训练阶段未出现过的类别的样本进行识别和分类，其主要思路是，借助类别语义信息，将可见类别的知识转移到未见类别中.提出了一种直推式的字典学习方法，包含以下两个步骤：首先，提出一个判别字典学习模型，对带标签的可见类别样本的视觉特征和类别语义特征建立映射关系模型；然后，针对可见类别和未见类别不同引起的域偏移问题，提出了一个基于直推学习的修正模型.通过在3个基准数据集（AwA，CUB和SUN）上的实验结果，证明了该方法的有效性和先进性.

Zero-Shot classification aims at recognizing instances from unseen categories that have no training instances in the training stage. To address this task, most existing approaches resort to class semantic information to transfer knowledge from the seen classes to the unseen ones. In this paper, a transductive dictionary learning approach is proposed to facilitate the task in two steps. A discriminative dictionary learning model is first proposed for constructing the relations between the visual modality and the class semantic modality with the labeled seen instances. Then a transductive modified model is used to alleviate the domain shift issue caused by the disjointness between the seen classes and the unseen classes. Experimental results on three benchmark datasets (AwA, CUB and SUN) demonstrate the effectiveness and superiority of the proposed approach.

国家自然科学基金（61771329，61472273）