Research progress of zero-shot learning

摘要

Although there have been encouraging breakthroughs in supervised learning since the renaissance of deep learning, the recognition of large-scale object classes remains a challenge, especially when some classes have no or few training samples. In this paper, the development of ZSL is reviewed comprehensively, including the evolution, key technologies, mainstream models, current research hotspots and future research directions. First, the evolution process is introduced from the perspectives of multi-shot, few-shot to zero-shot learning. Second, the key techniques of ZSL are analyzed in detail in terms of three aspects: visual feature extraction, semantic representation and visual-semantic mapping. Third, some typical models are interpreted in chronological order. Finally, closely related articles from the last three years are collected to analyze the current research hotspots and list future research directions.