Despite deep learning's widespread success, its data-hungry and computationally expensive nature makes it impractical for many data-constrained real-world applications. Few-Shot Learning (FSL) aims to address these limitations by enabling rapid adaptation to novel learning tasks, seeing significant growth in recent years. This survey provides a comprehensive overview of the field's latest advancements. Initially, FSL is formally defined, and its relationship with different learning fields is presented. A novel taxonomy is introduced, extending previously proposed ones, and real-world applications in classic and novel fields are described. Finally, recent trends shaping the field, outstanding challenges, and promising future research directions are discussed.

翻译：尽管深度学习已取得广泛成功，但其对数据的高度依赖和巨大的计算开销，使其在许多数据受限的现实应用中难以实用。少样本学习旨在通过使模型能够快速适应新学习任务来解决这些局限性，近年来取得了显著发展。本综述全面概述了该领域的最新进展。首先，对少样本学习进行了形式化定义，并阐述了其与不同学习领域的关系。本文提出了一种新的分类体系，对先前提出的分类方法进行了扩展，同时描述了其在经典领域和新兴领域的实际应用案例。最后，探讨了影响该领域发展的近期趋势、尚未解决的关键挑战以及未来具有前景的研究方向。