  Attribute-based Zero-Shot Learning (ZSL) has revolutionized the ability of
models to recognize new classes not seen during training. However, with the
advancement of large-scale models, the expectations have risen. Beyond merely
achieving zero-shot generalization, there is a growing demand for universal
models that can continually evolve in expert domains using unlabeled data. To
address this, we introduce a scaled-down instantiation of this challenge:
Evolutionary Generalized Zero-Shot Learning (EGZSL). This setting allows a
low-performing zero-shot model to adapt to the test data stream and evolve
online. We elaborate on three challenges of this special task, \ie,
catastrophic forgetting, initial prediction bias, and evolutionary data class
bias. Moreover, we propose targeted solutions for each challenge, resulting in
a generic method capable of continuous evolution from a given initial IGZSL
model. Experiments on three popular GZSL benchmark datasets demonstrate that
our model can learn from the test data stream while other baselines fail. Codes
are available at \url{https://github.com/cdb342/EGZSL}.
