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}.

翻译：基于属性的零样本学习（ZSL）彻底改变了模型识别训练中未见类别的能力。然而，随着大规模模型的发展，期望也随之提高。除了实现零样本泛化外，对能在专家领域利用无标签数据持续进化的通用模型的需求日益增长。为此，我们引入这一挑战的缩小规模实例：进化广义零样本学习（EGZSL）。该设定允许低性能的零样本模型适应测试数据流并在线进化。我们阐述了这一特殊任务的三个挑战，即灾难性遗忘、初始预测偏差和进化数据类别偏差。此外，我们针对每个挑战提出了针对性解决方案，从而得到一种能从给定初始IGZSL模型持续进化的通用方法。在三个主流GZSL基准数据集上的实验表明，我们的模型能从测试数据流中学习，而其他基线方法则失败。代码可在\url{https://github.com/cdb342/EGZSL}获取。