Abstract:Bidirectional mapping-based generative models have achieved remarkable performance for the generalized zero-shot learning (GZSL) recognition by learning to construct visual features from class semantics and reconstruct class semantics back from generated visual features. The performance of these models relies on the quality of synthesized features. This depends on the ability of the model to capture the underlying seen data distribution by relating semantic-visual spaces, learning discriminative information, a… Show more
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