Zero-shot learning (ZSL) aims to recognize unseen image classes without requiring any training samples of these specific classes. The ZSL problem is typically achieved by building up a semantic embedding space like attributes to bridge the visual features and class labels of images. Currently, most ZSL approaches focus on learning a visual-semantic alignment from seen classes using only the human-designed attributes, and then ZSL problem is solved by transferring semantic knowledge from seen classes to the unseen classes. However, few works indicate if the human-designed attributes are discriminative enough for image class prediction. To address this issue, we propose a semantic-aware dictionary learning (SADL) framework to explore these discriminative visual attributes across seen and unseen classes. Furthermore, the semantic cues are elegantly integrated into the feature representations via learned visual attributes for recognition task. Experiments conducted on two challenging benchmark datasets show that our approach outweighs other state-of-the-art ZSL methods.

零镜头学习（ZSL）旨在识别看不见的图像类别，而无需这些特定类别的任何训练样本。ZSL问题通常是通过建立语义嵌入空间（例如属性）来桥接图像的视觉特征和类标签来实现的。当前，大多数ZSL方法专注于仅使用人工设计的属性来从可见类中学习视觉语义对齐，然后通过将语义知识从可见类转移到不可见类中来解决ZSL问题。但是，很少有作品表明人为设计的属性对于图像类别预测是否具有足够的区分性。为了解决此问题，我们提出了一种语义感知词典学习（SADL）框架，以探索可见和不可见类之间的这些区分性视觉属性。此外，通过学习的视觉属性将语义线索优雅地集成到特征表示中，以进行识别任务。在两个具有挑战性的基准数据集上进行的实验表明，我们的方法胜过其他最新的ZSL方法。