  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.
