Zero-shot learning (ZSL) is a promising approach to generalizing a model to categories unseen during training by leveraging class attributes, but challenges remain. Recently, methods using generative models to combat bias towards classes seen during training have pushed state of the art, but these generative models can be slow or computationally expensive to train. Also, these generative models assume that the attribute vector of each unseen class is available a priori at training, which is not always practical. Additionally, while many previous ZSL methods assume a one-time adaptation to unseen classes, in reality, the world is always changing, necessitating a constant adjustment of deployed models. Models unprepared to handle a sequential stream of data are likely to experience catastrophic forgetting. We propose a Meta-learned Attribute self-Interaction Network (MAIN) for continual ZSL. By pairing attribute self-interaction trained using meta-learning with inverse regularization of the attribute encoder, we are able to outperform state-of-the-art results without leveraging the unseen class attributes while also being able to train our models substantially faster (>100x) than expensive generative-based approaches. We demonstrate this with experiments on five standard ZSL datasets (CUB, aPY, AWA1, AWA2, and SUN) in the generalized zero-shot learning and continual (fixed/dynamic) zero-shot learning settings. Extensive ablations and analyses demonstrate the efficacy of various components proposed.

翻译：零样本学习（ZSL）是一种通过利用类别属性将模型泛化到训练中未见类别的有前景方法，但仍存在诸多挑战。近年来，采用生成模型来缓解对训练中可见类别偏倚的方法推动了该领域的技术发展，但这类生成模型训练过程可能缓慢且计算成本高昂。此外，这些生成模型假设每个未见类别的属性向量在训练时已预先可得，这在实际应用中并不总是可行的。同时，尽管许多现有ZSL方法假设模型一次性适应未见类别，但现实世界始终处于动态变化中，这要求已部署模型进行持续调整。若模型未准备处理序列化的数据流，则很可能遭遇灾难性遗忘。为此，我们提出一种用于持续ZSL的元学习属性自交互网络（MAIN）。通过将经元学习训练的属性自交互与属性编码器的逆正则化相结合，我们无需利用未见类别属性即可超越当前最优结果，同时模型训练速度较昂贵的生成式方法显著提升（>100倍）。我们在五个标准ZSL数据集（CUB、aPY、AWA1、AWA2和SUN）上，针对广义零样本学习和持续（固定/动态）零样本学习场景进行了实验验证。广泛的消融实验与分析证明了所提出各模块的有效性。