Fine-Grained Zero-Shot Learning: Advances, Challenges, and Prospects

• URL: http://arxiv.org/abs/2401.17766v2
• Date: Sun, 4 Feb 2024 05:57:12 GMT
• Title: Fine-Grained Zero-Shot Learning: Advances, Challenges, and Prospects
• Authors: Jingcai Guo, Zhijie Rao, Zhi Chen, Jingren Zhou, Dacheng Tao
• Abstract summary: We present a broad review of recent advances for fine-grained analysis in zero-shot learning (ZSL) 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.
• Score: 84.36935309169567
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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