# Envisioning Class Entity Reasoning by Large Language Models for Few-shot Learning Source: [Envisioning Class Entity Reasoning by Large Language Models for Few-shot Learning]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:39:01.541Z Authors: Mushui Liu, Fangtai Wu, Bozheng Li, Ziqian Lu, Yunlong Yu, Xi Li Published: 2024-08-22T15:10:20.000Z ## Abstract Few-shot learning (FSL) aims to recognize new concepts using a limited number of visual samples. Existing approaches attempt to incorporate semantic information into the limited visual data for category understanding. However, these methods often enrich class-level feature representations with abstract category names, failing to capture the nuanced features essential for effective generalization. To address this issue, we propose a novel framework for FSL, which incorporates both the abstract class semantics and the concrete class entities extracted from Large Language Models (LLMs), to enhance the representation of the class prototypes. Specifically, our framework composes a Semantic-guided Visual Pattern Extraction (SVPE) module and a Prototype-Calibration (PC) module, where the SVPE meticulously extracts semantic-aware visual patterns across diverse scales, while the PC module seamlessly integrates these patterns to refine the visual prototype, enhancing its representativeness. Extensive experiments on four few-shot classification benchmarks and the BSCD-FSL cross-domain benchmarks showcase remarkable advancements over the current state-of-the-art methods. Notably, for the challenging one-shot setting, our approach, utilizing the ResNet-12 backbone, achieves an impressive average improvement of 1.95% over the second-best competitor. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on FC100](): Image Classification; FC100; Accuracy: 57.34. - [Image Classification on miniImageNet](): Image Classification; MiniImagenet; Accuracy: 81.14. - [Image Classification on CIFAR FS](): Image Classification; CIFAR-FS; Accuracy: 86.01. - [Image Classification on tieredImageNet](): Image Classification; tieredImageNet; Accuracy: 81.81. - [Image Classification on Cross-domain Few-Shot Learning Suite (CUB, Cars, Places, Plantae, EuroSAT, CropDisease) (test)](): Image Classification; Cross-domain Few-Shot Learning Suite (CUB, Cars, Places, Plantae, EuroSAT, CropDisease) (test); EuroSAT Accuracy: 74.13. ## Original sources and implementation [Original paper]() [PDF]()