  Zero-Shot Learning (ZSL) aims to enable classifiers to identify unseen
classes by enhancing data efficiency at the class level. This is achieved by
generating image features from pre-defined semantics of unseen classes.
However, most current approaches heavily depend on the number of samples from
seen classes, i.e. they do not consider instance-level effectiveness. In this
paper, we demonstrate that limited seen examples generally result in
deteriorated performance of generative models. To overcome these challenges, we
propose ZeroDiff, a Diffusion-based Generative ZSL model. This unified
framework incorporates diffusion models to improve data efficiency at both the
class and instance levels. Specifically, for instance-level effectiveness,
ZeroDiff utilizes a forward diffusion chain to transform limited data into an
expanded set of noised data. For class-level effectiveness, we design a
two-branch generation structure that consists of a Diffusion-based Feature
Generator (DFG) and a Diffusion-based Representation Generator (DRG). DFG
focuses on learning and sampling the distribution of cross-entropy-based
features, whilst DRG learns the supervised contrastive-based representation to
boost the zero-shot capabilities of DFG. Additionally, we employ three
discriminators to evaluate generated features from various aspects and
introduce a Wasserstein-distance-based mutual learning loss to transfer
knowledge among discriminators, thereby enhancing guidance for generation.
Demonstrated through extensive experiments on three popular ZSL benchmarks, our
ZeroDiff not only achieves significant improvements over existing ZSL methods
but also maintains robust performance even with scarce training data. Code will
be released upon acceptance.
