Attribute-Modulated Generative Meta Learning for Zero-Shot Classification

Zero-shot learning (ZSL) aims to transfer knowledge from seen classes to semantically related unseen classes, which are absent during training. The promising strategies for ZSL are to synthesize visual features of unseen classes conditioned on semantic side information and to incorporate meta-learning to eliminate the model's inherent bias towards seen classes. Existing meta generative approaches pursue a common model shared across task distributions; in contrast, we aim to construct a generative network adaptive to task characteristics. To this end, we propose the Attribute-Modulated generAtive meta-model for Zero-shot learning (AMAZ). Our model consists of an attribute-aware modulation network and an attribute-augmented generative network. Given unseen classes, the modulation network adaptively modulates the generator by applying task-specific transformations so that the generative network can adapt to highly diverse tasks. Our empirical evaluations on four widely-used benchmarks show that AMAZ improves state-of-the-art methods by 3.8% and 5.1% in ZSL and generalized ZSL settings, respectively, demonstrating the superiority of our method.