Recent progress towards learning from limited supervision has encouraged
efforts towards designing models that can recognize novel classes at test time
(generalized zero-shot learning or GZSL). GZSL approaches assume knowledge of
all classes, with or without labeled data, beforehand. However, practical
scenarios demand models that are adaptable and can handle dynamic addition of
new seen and unseen classes on the fly (that is continual generalized zero-shot
learning or CGZSL). One solution is to sequentially retrain and reuse
conventional GZSL methods, however, such an approach suffers from catastrophic
forgetting leading to suboptimal generalization performance. A few recent
efforts towards tackling CGZSL have been limited by difference in settings,
practicality, data splits and protocols followed-inhibiting fair comparison and
a clear direction forward. Motivated from these observations, in this work, we
firstly consolidate the different CGZSL setting variants and propose a new
Online-CGZSL setting which is more practical and flexible. Secondly, we
introduce a unified feature-generative framework for CGZSL that leverages
bi-directional incremental alignment to dynamically adapt to addition of new
classes, with or without labeled data, that arrive over time in any of these
CGZSL settings. Our comprehensive experiments and analysis on five benchmark
datasets and comparison with baselines show that our approach consistently
outperforms existing methods, especially on the more practical Online setting.

本研究提出了一种新的在线学习方法，旨在解决 continual generalized zero-shot learning 中动态添加新已知或未知类别所带来的挑战，该方法结合了双向增量对齐和特征生成框架以动态适应新类别的到来，并在五个基准数据集上表现出更好的泛化能力。