Embedding-aware generative model (EAGM) addresses the data insufficiency problem for zero-shot learning (ZSL) by constructing a generator between semantic and visual embedding spaces. Thanks to the predefined benchmark and protocols, the number of proposed EAGMs for ZSL is increasing rapidly. We argue that it is time to take a step back and reconsider the embedding-aware generative paradigm. The purpose of this paper is three-fold. First, given the fact that the current embedding features in benchmark datasets are somehow out-of-date, we improve the performance of EAGMs for ZSL remarkably with embarrassedly simple modifications on the embedding features. This is an important contribution, since the results reveal that the embedding of EAGMs deserves more attention. Second, we compare and analyze a significant number of EAGMs in depth. Based on five benchmark datasets, we update the state-of-the-art results for ZSL and give a strong baseline for few-shot learning (FSL), including the classic unseen-class few-shot learning (UFSL) and the more challenging seen-class few-shot learning (SFSL). Finally, a comprehensive generative model repository, namely, generative any-shot learning (GASL) repository, is provided, which contains the models, features, parameters, and settings of EAGMs for ZSL and FSL. Any results in this paper can be readily reproduced with only one command line based on GASL.

翻译：嵌入感知生成模型通过构建语义与视觉嵌入空间之间的生成器，解决了零样本学习中的数据不足问题。得益于预定义的基准测试和协议，针对零样本学习的嵌入感知生成模型数量正在迅速增加。我们认为，现在是时候退一步，重新审视嵌入感知生成范式。本文的目的有三方面：首先，鉴于当前基准数据集中的嵌入特征已略显过时，我们通过对嵌入特征进行简单却显著有效的修改，大幅提升了嵌入感知生成模型在零样本学习中的性能。这是一项重要贡献，因为结果表明嵌入感知生成模型的嵌入特征值得更多关注。其次，我们深度比较和分析了大量嵌入感知生成模型。基于五个基准数据集，我们更新了零样本学习的最新成果，并为小样本学习（包括经典未见类小样本学习和更具挑战性的可见类小样本学习）提供了强基线。最后，我们提供了一个综合生成模型库，即生成式任意样本学习库，其中包含嵌入感知生成模型用于零样本学习和小样本学习的模型、特征、参数及设置。基于该库，本文的所有结果仅需一条命令即可轻松复现。