Recent zero-shot learning (ZSL) approaches have integrated fine-grained analysis, i.e., fine-grained ZSL, to mitigate the commonly known seen/unseen domain bias and misaligned visual-semantics mapping problems, and have made profound progress. Notably, this paradigm differs from existing close-set fine-grained methods and, therefore, can pose unique and nontrivial challenges. However, to the best of our knowledge, there remains a lack of systematic summaries of this topic. To enrich the literature of this domain and provide a sound basis for its future development, in this paper, we present a broad review of recent advances for fine-grained analysis in ZSL. Concretely, we first provide a taxonomy of existing methods and techniques with a thorough analysis of each category. Then, we summarize the benchmark, covering publicly available datasets, models, implementations, and some more details as a library. Last, we sketch out some related applications. In addition, we discuss vital challenges and suggest potential future directions.

翻译：近期零样本学习（ZSL）方法引入了细粒度分析，即细粒度零样本学习，以缓解已知的已见/未见领域偏差及视觉-语义映射对齐问题，并取得了重大进展。值得注意的是，该范式不同于现有的闭集细粒度方法，因此能够带来独特且非平凡的研究挑战。然而，据我们所知，目前仍缺乏对该主题的系统性综述。为丰富该领域文献并为其未来发展奠定坚实基础，本文对零样本学习中细粒度分析的近期进展进行了广泛综述。具体而言，我们首先提供现有方法与技术的分类体系，并对每类方法进行深入分析；随后，总结基准测试内容，涵盖公开数据集、模型、实现方案及作为资源库的更多细节；最后，概述相关应用。此外，我们探讨了关键挑战并提出了潜在未来方向。