Exploiting Semantic-Visual Symmetry for Goal-Oriented Zero-Shot Recognition

Traditional machine learning methods only classify the instances whose classes are seen during training. In practice, many applications require to recognize the classes unknown in the training stage. In order to tackle this kind of challenging task, zero-shot learning is introduced, which incorporates additional semantic information to establish a semantic-visual symmetry, thereby facilitating the transfer of knowledge from known to unknown classes. Although user-defined attributes are commonly utilized to provide prior semantic information for zero-shot recognition, their importance for discrimination is not always consistent. Motivated by the observation that there exist both latent discriminative features and attributes in the images, this paper proposes a goal-oriented joint learning architecture to establish the symmetric relationships between images, attributes and categories for zero-shot learning. To be more specific, we model the latent feature and attribute spaces using the dictionary learning architecture. To learn the symmetric relationships between latent features and latent attributes, a linear transformation is applied while maintaining the semantic information. Moreover, seen-class classifiers are trained to enhance the discriminability of latent features. Extensive experiments on three representative benchmark datasets show that the proposed algorithm outperforms existing methods, highlighting the effectiveness of modeling explicit symmetry in the semantic-visual space for robust zero-shot knowledge transfer.