  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.
