Human intelligence is characterized by our ability to absorb and apply knowledge from the world around us, especially in rapidly acquiring new concepts from minimal examples, underpinned by prior knowledge. Few-shot learning (FSL) aims to mimic this capacity by enabling significant generalizations and transferability. However, traditional FSL frameworks often rely on assumptions of clean, complete, and static data, conditions that are seldom met in real-world environments. Such assumptions falter in the inherently uncertain, incomplete, and dynamic contexts of the open world. This paper presents a comprehensive review of recent advancements designed to adapt FSL for use in open-world settings. We categorize existing methods into three distinct types of open-world few-shot learning: those involving varying instances, varying classes, and varying distributions. Each category is discussed in terms of its specific challenges and methods, as well as its strengths and weaknesses. We standardize experimental settings and metric benchmarks across scenarios, and provide a comparative analysis of the performance of various methods. In conclusion, we outline potential future research directions for this evolving field. It is our hope that this review will catalyze further development of effective solutions to these complex challenges, thereby advancing the field of artificial intelligence.

翻译：人类智能的特点在于我们能够吸收并应用周围世界的知识，尤其是在先验知识的支撑下，从少量示例中快速掌握新概念。少样本学习旨在模仿这种能力，实现显著的泛化与迁移。然而，传统的少样本学习框架通常依赖于数据干净、完整且静态的假设，这些条件在现实环境中很少得到满足。在开放世界固有的不确定、不完整和动态情境下，此类假设往往失效。本文全面综述了为适应开放世界环境而设计的少样本学习最新进展。我们将现有方法归纳为三种开放世界少样本学习类型：涉及变化实例、变化类别和变化分布的方法。针对每一类别，我们讨论了其具体挑战与方法，以及各自的优势与不足。我们统一了不同场景下的实验设置与度量基准，并对各类方法的性能进行了比较分析。最后，我们展望了这一发展领域未来可能的研究方向。我们希望本综述能够推动针对这些复杂挑战的有效解决方案的进一步发展，从而推动人工智能领域的进步。