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.
While existing meta generative approaches pursue a common model shared across
task distributions, we aim to construct a generative network adaptive to task
characteristics. To this end, we propose an Attribute-Modulated generAtive
meta-model for Zero-shot learning (AMAZ). Our model consists of an
attribute-aware modulation network, an attribute-augmented generative network,
and an attribute-weighted classifier. 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. The weighted classifier utilizes the data quality to enhance the
training procedure, further improving the model performance. Our empirical
evaluations on four widely-used benchmarks show that AMAZ outperforms
state-of-the-art methods by 3.8% and 3.1% in ZSL and generalized ZSL settings,
respectively, demonstrating the superiority of our method. Our experiments on a
zero-shot image retrieval task show AMAZ's ability to synthesize instances that
portray real visual characteristics.
