关键词: 零样本学习, 知识迁移, 双字典, 共有结构, 局部敏感

Abstract: Zero-Shot Learning（ZSL） has utilized association model of visual and semantic features to transfer discriminative knowledge. But visual and semantic data is not a simple correspondence, it’s difficult to directly establish the mapping. A locality sensitive double dictionary method is proposed, which has two main contributions：（1） Double dictionary method. Visual-semantic single dictionary mapping lacks direct associated common variables, a double dictionary method is proposed to add a descriptive dictionary of common structure for visual and semantic features, a more reasonable visual-semantic association channel is constructed. （2） Locality sensitive manifold preserving method. In the double dictionary learning, the description of local structure information is vital. A manifold structure graph is constructed to define locality sensitive regularization, and to jointly optimize the dictionary learning and local manifold preserving. The experimental results on AwA and CUB datasets show the proposed method outperforms the compared algorithms in accuracy.

Key words: zero-shot learning, knowledge transfer, double dictionary, common structure, locality sensitive