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 to open-world environments. We categorize existing methods into three distinct types of FSL in the open world: 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.

Few-shot open-set recognition (FSOSR) has become a great challenge, which requires classifying known classes and rejecting the unknown ones with only limited samples. Existing FSOSR methods mainly construct an ambiguous distribution of known classes from scarce known samples without considering the latent distribution information of unknowns, which degrades the performance of open-set recognition. To address this issue, we propose a novel loss function called multi-relation margin (MRM) loss that can plug in few-shot methods to boost the performance of FSOSR. MRM enlarges the margin between different classes by extracting the multi-relationship of paired samples to dynamically refine the decision boundary for known classes and implicitly delineate the distribution of unknowns. Specifically, MRM separates the classes by enforcing a margin while concentrating samples of the same class on a hypersphere with a learnable radius. In order to better capture the distribution information of each class, MRM extracts the similarity and correlations among paired samples, ameliorating the optimization of the margin and radius. Experiments on public benchmarks reveal that methods with MRM loss can improve the unknown detection of AUROC by a significant margin while correctly classifying the known classes.