Few-Shot Learning: Unlocking AI’s Potential in a Data-Scarce World

Latest 50 papers on few-shot learning: Sep. 14, 2025

Few-Shot Learning: Unlocking AI’s Potential in a Data-Scarce World

In the rapidly evolving landscape of AI and Machine Learning, the quest for models that can learn from minimal data is more critical than ever. Traditional deep learning often demands vast, meticulously labeled datasets—a luxury rarely available in specialized domains. This challenge has propelled Few-Shot Learning (FSL) to the forefront of AI research, promising a future where models can adapt and generalize with human-like efficiency. Recent breakthroughs, as showcased in a collection of cutting-edge research papers, are not only pushing the boundaries of what’s possible but also bringing FSL closer to real-world deployment across diverse applications, from healthcare to robotics and cybersecurity.

The Big Ideas & Core Innovations

The central theme across these papers is overcoming data scarcity and enhancing generalization, often by leveraging pre-trained models and innovative adaptation strategies. Many works focus on improving the efficiency and interpretability of FSL. For instance, in “From Channel Bias to Feature Redundancy: Uncovering the ‘Less is More’ Principle in Few-Shot Learning”, researchers from UESTC, The University of Hong Kong, and Tongji University highlight that too many features can be detrimental in low-data scenarios, proposing the AFIA (Augmented Feature Importance Adjustment) method to selectively reduce harmful redundancy. This ‘less is more’ principle is a significant insight, suggesting that quality over quantity in features is paramount for FSL.

Simultaneously, the integration of expert knowledge and domain-specific pre-training is proving transformative. Uddin et al. from MICCAI Workshop on Data Engineering in Medical Imaging 2025 in “Expert-Guided Explainable Few-Shot Learning for Medical Image Diagnosis” integrate radiologist annotations via an explanation loss to align model attention with clinically meaningful regions, boosting both accuracy and interpretability in medical imaging with limited data. Similarly, Author A et al. from Institution X, Y, Z in “Exploring Pre-training Across Domains for Few-Shot Surgical Skill Assessment” demonstrate that domain-specific pre-training significantly enhances surgical skill assessment, underscoring the critical role of the domain gap in FSL performance.

Another innovative trend is the fusion of modalities and model architectures. Ghassen Baklouti et al. from École de Technologie Supérieure introduce LIMO in “Language-Aware Information Maximization for Transductive Few-Shot CLIP”, which uses information-theoretic concepts to boost transductive FSL for vision-language models (VLMs). This highlights how leveraging the interplay between modalities can unlock superior performance. In the realm of robotics, Zhiyuan Li et al. from MIT, Stanford, and Georgia Tech present O3Afford in “O³Afford: One-Shot 3D Object-to-Object Affordance Grounding for Generalizable Robotic Manipulation”, a one-shot framework that combines semantic features from vision foundation models with LLMs to enhance 3D spatial understanding and robotic manipulation. This cross-modal synergy is paving the way for more intelligent and adaptable robotic systems.

Under the Hood: Models, Datasets, & Benchmarks

These advancements are built upon robust models, innovative datasets, and rigorous benchmarks designed to tackle the unique challenges of few-shot scenarios:

• AFIA (Augmented Feature Importance Adjustment): Proposed by Ji Zhang et al. for vision models, AFIA leverages data augmentation to reduce feature redundancy, with code available at https://github.com/JiZhang-UESTC/Augmented-Feature-Importance-Adjustment.
• RRDataset: Introduced by Chunxiao Li et al. from Beijing Normal University and others in “Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios”, this comprehensive benchmark evaluates AI-generated image detection under challenging real-world conditions like internet transmission and re-digitization, emphasizing the power of human few-shot learning.
• CoFi (Coarse-to-Fine pipeline): Developed by Hongjin Fang et al. from Cornell University and Weill Cornell Medicine in “CoFi: A Fast Coarse-to-Fine Few-Shot Pipeline for Glomerular Basement Membrane Segmentation”, CoFi is a fast few-shot segmentation pipeline for medical images that uses lightweight models and automated prompt generation via SAM, with code at https://github.com/ddrrnn123/CoFi.
• MedSpaformer: Proposed by Jiexia Ye et al. from The Hong Kong University of Science and Technology in “MedSpaformer: a Transferable Transformer with Multi-granularity Token Sparsification for Medical Time Series Classification”, this transformer architecture uses a sparse dual-attention mechanism for efficient medical time series classification, demonstrating strong zero-shot capabilities.
• QAgent: From Zhenxiao Fu et al. at Indiana University Bloomington, “QAgent: An LLM-based Multi-Agent System for Autonomous OpenQASM programming” integrates task planning, FSL, and RAG to automate OpenQASM programming, achieving significant improvements in quantum circuit code generation, with code at https://github.com/fuzhenxiao/QCoder.
• WEBEYETRACK: An open-source, browser-friendly framework by Eduardo Davalos et al. from Trinity University and Vanderbilt University for few-shot gaze estimation with on-device personalization. It includes BlazeGaze, a lightweight CNN model, and offers code at https://github.com/RedForestAI/WebEyeTrack.
• TransMatch: Introduced by Mohsen Asghari Ilani and Yaser Mike Banad from the University of Oklahoma in “TransMatch: A Transfer-Learning Framework for Defect Detection in Laser Powder Bed Fusion Additive Manufacturing”, this framework combines semi-supervised and few-shot learning for highly accurate defect detection in additive manufacturing, with code at https://github.com/transmatch-framework/.
• MSEF (Multi-layer Steerable Embedding Fusion): Developed by Zhuomin Chen et al. from Sun Yat-Sen University and National University of Singapore in “Integrating Time Series into LLMs via Multi-layer Steerable Embedding Fusion for Enhanced Forecasting”, MSEF enhances LLMs for time series forecasting by integrating temporal patterns across multiple layers, achieving SOTA in few-shot scenarios, with code at https://github.com/One1sAll/MSEF.

Impact & The Road Ahead

The collective impact of this research is profound. Few-shot learning is no longer a niche academic pursuit; it’s rapidly maturing into a practical paradigm for developing agile, data-efficient AI systems. The applications are boundless:

• Healthcare: From diagnosing rare diseases with limited patient data (e.g., Cough Classification using Few-Shot Learning, Expert-Guided Explainable Few-Shot Learning for Medical Image Diagnosis, Glo-VLMs: Leveraging Vision-Language Models for Fine-Grained Diseased Glomerulus Classification) to automating surgical skill assessment (Exploring Pre-training Across Domains for Few-Shot Surgical Skill Assessment), FSL is critical for delivering personalized and accessible medical AI.
• Robotics & Manufacturing: Enabling robots to quickly adapt to new manipulation tasks with minimal examples (O³Afford, In-Context Iterative Policy Improvement for Dynamic Manipulation, Embodied Long Horizon Manipulation) and improving defect detection in advanced manufacturing (TransMatch) will drive automation and efficiency.
• Natural Language Processing & Vision-Language Models: Enhancing the ability of LLMs to generate high-quality clinical summaries (MaLei at MultiClinSUM), measure symptom severity in mental health (Using Large Language Models to Measure Symptom Severity in Patients At Risk for Schizophrenia), and improve cross-domain stance detection (MLSD) are expanding the reach of AI-driven communication and analysis.
• Cybersecurity & Networking: Novel applications like FlowletFormer (Network Behavioral Semantic Aware Pre-training Model for Traffic Classification) and ThreatGPT (An Agentic AI Framework for Enhancing Public Safety through Threat Modeling) show how FSL can bolster real-time threat detection and network management.

The road ahead involves further refining generalization capabilities, ensuring ethical deployment, and developing more robust theoretical foundations for FSL, as explored by “Curvature Learning for Generalization of Hyperbolic Neural Networks” and “Learnable Loss Geometries with Mirror Descent for Scalable and Convergent Meta-Learning”. As AI systems become more ubiquitous, the ability to learn and adapt with minimal supervision will be paramount, moving us closer to a future where AI truly augments human intelligence. The advancements highlighted here are not just incremental steps; they are powerful leaps towards AI that is truly smart, efficient, and applicable across every facet of our lives.