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Learning Class Prototypes via Structure Alignment for Zero-Shot Recognition
[chapter]
2018
Lecture Notes in Computer Science
Zero-shot learning (ZSL) aims to recognize objects of novel classes without any training samples of specific classes, which is achieved by exploiting the semantic information and auxiliary datasets. Recently most ZSL approaches focus on learning visual-semantic embeddings to transfer knowledge from the auxiliary datasets to the novel classes. However, few works study whether the semantic information is discriminative or not for the recognition task. To tackle such problem, we propose a coupled
doi:10.1007/978-3-030-01249-6_8
fatcat:kwlsx742ffe7xi3okrnloqj74u