FSIGenZ synthesizes a small set of semantic prototypes per unseen class and trains a contrastive classifier with semantic regularization, achieving competitive ZSL accuracy on SUN, AwA2, and CUB with far fewer synthetic features.
Feature generating networks for zero-shot learning
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Few-Shot Inspired Generative Zero-Shot Learning
FSIGenZ synthesizes a small set of semantic prototypes per unseen class and trains a contrastive classifier with semantic regularization, achieving competitive ZSL accuracy on SUN, AwA2, and CUB with far fewer synthetic features.