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Embedding-aware generative model (EAGM) addresses the data insufficiencyproblem for zero-shot learning (ZSL) by constructing a generator betweensemantic and visual feature spaces. Thanks to the predefined benchmark andprotocols, the number of proposed EAGMs for ZSL is increasing rapidly. We arguethat it is time to take a step back and reconsider the embedding-awaregenerative paradigm. The main work of this paper is two-fold. First, theembedding features in benchmark datasets are somehow overlooked, whichpotentially limits the performance of EAGMs, while most researchers focus onhow to improve EAGMs. Therefore, we conduct a systematic evaluation of tenrepresentative EAGMs and prove that even embarrassedly simple modifications onthe embedding features can improve the performance of EAGMs for ZSL remarkably.So it's time to pay more attention to the current embedding features inbenchmark datasets. Second, based on five benchmark datasets, each with sixany-shot learning scenarios, we systematically compare the performance of tentypical EAGMs for the first time, and we give a strong baseline for zero-shotlearning (ZSL) and few-shot learning (FSL). Meanwhile, a comprehensivegenerative model repository, namely, generative any-shot learning (GASL)repository, is provided, which contains the models, features, parameters, andscenarios of EAGMs for ZSL and FSL. Any results in this paper can be readilyreproduced with only one command line based on GASL.

A Systematic Evaluation and Benchmark for Embedding-Aware Generative Models: Features, Models, and Any-shot Scenarios