Generative Zero Shot Learning

Generative zero-shot learning (GZSL) aims to enable classifiers to recognize objects from unseen classes using only their semantic descriptions, without requiring any training examples. Current research focuses on improving the data efficiency of generative models, particularly diffusion models and generative adversarial networks (GANs), often incorporating techniques like dynamic semantic prototypes and attribute augmentation to synthesize realistic and diverse visual features for unseen classes. These advancements address limitations in existing methods, such as overfitting to seen classes and reliance on limited semantic information, leading to more robust and accurate zero-shot classification. The resulting improvements have significant implications for various applications, including image recognition and object detection in scenarios with limited labeled data.