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.

Recently, noisy few-shot learning (NFSL) has been exploring the model robustness to label noise, breaking the limitation of completely accurate labeling in small sample scenarios. Existing NFSL methods directly employ delicately designed pseudo-noise to simulate and adapt to noisy environments. However, determining the optimal combination of pseudo-noise is challenging and improperly configuring pseudo-noise may lead to adverse effects on the training models. To deal with the problems, this letter proposes a novelAdaptiveMultI-viewDenoisingEvaluation (AMIDE) framework, which establishes an adaptive and robust embedding and classifier without relying on pseudo-noise. In the training phase, we design an adaptive label smoothing scheme, where soft labels with learnable smooth coefficients are inferred from data distribution to mitigate overconfident labeling. In the testing stage, we propose a multi-view fused evaluation scheme, where different network layers are treated as distinct views to generate potential clean features and modify prototypes, thereby enhancing the accuracy of evaluation. In this way, the impact of noise is effectively alleviated from two perspectives. Extensive experiments on several few-shot classification benchmarks show the superiority and robustness of our method.