  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.
