Few-Shot Learning: Navigating the AI Frontier with Minimal Data, Maximum Impact

Latest 50 papers on few-shot learning: Dec. 21, 2025

Few-shot learning (FSL) is rapidly becoming a cornerstone of adaptable and efficient AI, addressing the critical challenge of building robust models with limited labeled data. In a world where data annotation is often expensive, time-consuming, or simply unavailable, FSL offers a compelling path forward. Recent research showcases a vibrant landscape of innovation, extending FSL’s reach across diverse domains, from medical imaging to robotic control and beyond. This post dives into a collection of breakthroughs that are pushing the boundaries of what’s possible with scarce data.

The Big Idea(s) & Core Innovations

The overarching theme uniting recent FSL advancements is the quest for models that can generalize effectively from just a handful of examples. A key strategy involves leveraging the immense power of pre-trained models, especially Large Language Models (LLMs) and Vision-Language Models (VLMs), and intelligently adapting them to new tasks.

For instance, in the realm of human-robot interaction, researchers from Yale University in their paper, “Few-Shot Inference of Human Perceptions of Robot Performance in Social Navigation Scenarios”, demonstrate that LLMs can predict human perceptions of robot performance with significantly fewer labeled examples than traditional supervised methods. This points towards more personalized and adaptable robotic systems.

In medical applications, privacy and data scarcity are paramount. A framework from Pro2Future GmbH and Johannes Kepler University Linz, detailed in “Federated Few-Shot Learning for Epileptic Seizure Detection Under Privacy Constraints”, combines federated learning with FSL to enable personalized seizure detection without centralizing sensitive EEG data. Similarly, for cardiac MRI segmentation, “PathCo-LatticE: Pathology-Constrained Lattice-Of Experts Framework for Fully-supervised Few-Shot Cardiac MRI Segmentation” introduces a novel lattice-of-experts architecture that integrates pathology constraints, significantly boosting performance in low-data scenarios.

Computer vision is seeing a surge of FSL innovation. Fudan University’s “Domain-RAG: Retrieval-Guided Compositional Image Generation for Cross-Domain Few-Shot Object Detection” proposes a training-free framework for cross-domain few-shot object detection (CD-FSOD), generating domain-consistent synthetic data. This is critical for tasks like remote sensing where acquiring diverse labeled data is challenging. Another significant step in computer vision comes from Texas A&M University, whose paper “Surely Large Multimodal Models (Don’t) Excel in Visual Species Recognition?” introduces Post-hoc Correction (POC), a method that leverages LMMs to enhance FSL predictions in Visual Species Recognition (VSR), achieving notable accuracy gains without retraining.

Further optimizing existing models, Huazhong University of Science and Technology and Peking University address template bias in CLIP with “Decoupling Template Bias in CLIP: Harnessing Empty Prompts for Enhanced Few-Shot Learning”, using ‘empty prompts’ to improve classification accuracy and robustness. This speaks to the subtle yet powerful ways prompt engineering is refining FSL.

For practical, resource-constrained environments, Siegfried Ludwig et al. from Imperial College London’s “EEG-D3: A Solution to the Hidden Overfitting Problem of Deep Learning Models” presents an interpretable architecture that disentangles latent brain dynamics, enabling effective FSL for sleep stage classification. This tackles the often-overlooked ‘hidden overfitting’ problem, improving real-world applicability.

Under the Hood: Models, Datasets, & Benchmarks

These advancements are often powered by innovative architectures, specialized datasets, and rigorous benchmarks:

• Augmented SEAN TOGETHER dataset (v2): Introduced in “Few-Shot Inference of Human Perceptions of Robot Performance in Social Navigation Scenarios” by Yale University, this dataset provides real-world human-robot interaction data for social navigation, crucial for training LLMs to understand human perceptions.
• Post-hoc Correction (POC): Proposed by Tian Liu et al. from Texas A&M University in “Surely Large Multimodal Models (Don’t) Excel in Visual Species Recognition?” (code: https://tian1327.github.io/POC), this method enhances existing FSL models for Visual Species Recognition (VSR) without additional training.
• FSL-HDnn Accelerator: Developed by Yue Wang et al. from the University of California, Berkeley, and others, in “FSL-HDnn: A 40 nm Few-shot On-Device Learning Accelerator with Integrated Feature Extraction and Hyperdimensional Computing”, this 40nm chip combines feature extraction with hyperdimensional computing for efficient on-device FSL, ideal for low-power edge AI applications.
• SWIFT (Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective): A method from Tian Liu et al. affiliated with Texas A&M University (code: https://github.com/OSU), that leverages open-source VLMs and their pretraining data, along with classifier initialization and temperature tuning, to significantly boost semi-supervised FSL performance in auto-annotation. The project website is at https://tian1327.github.io/SWIFT.
• SSL-MedSAM2 Framework: Introduced by Z. Gong and X. Chen from the University of Nottingham in “SSL-MedSAM2: A Semi-supervised Medical Image Segmentation Framework Powered by Few-shot Learning of SAM2” (code: https://github.com/naisops/SSL-MedSAM2/), this combines MedSAM2 and nnU-Net for semi-supervised medical image segmentation, generating high-quality pseudo-labels with minimal annotations.
• Logos Dataset for Russian Sign Language: Introduced by Ilya Ovodov et al. from SberAI in “Logos as a Well-Tempered Pre-train for Sign Language Recognition”, this extensive dataset for Russian Sign Language recognition is enhancing cross-language transfer learning for sign language recognition (SLR).
• Domain-RAG: A training-free compositional image generation framework from Yu Li et al. at Fudan University (code: https://github.com/LiYu0524/Domain-RAG), specifically designed for cross-domain few-shot object detection (CD-FSOD).
• CuRPO (Curriculum-based Relative Policy Optimization): Developed by Qingyang Yan et al. from Huazhong University of Science and Technology (code: https://github.com/qyoung-yan/CuRPO), this curriculum learning approach improves visual grounding by leveraging Chain-of-Thought (CoT) reasoning and reward signals.
• ABounD Framework: From Runzhi Deng et al. at Nanjing University (code: https://github.com/ABounD), this framework for few-shot multi-class anomaly detection combines Dynamic Concept Fusion (DCF) and Adversarial Boundary Forging (ABF) for robust performance on industrial datasets like MVTec-AD and VisA.

Impact & The Road Ahead

The impact of these few-shot learning breakthroughs is profound, promising more accessible, adaptable, and privacy-preserving AI systems. From enabling personalized medical diagnostics with minimal patient data to enhancing robotics in complex social settings and making vision systems more robust in real-world conditions, FSL is empowering AI to tackle problems previously deemed intractable due to data scarcity. The ability to quickly adapt models to new tasks with limited examples will accelerate innovation across industries, from agriculture (e.g., “A Domain-Adapted Lightweight Ensemble for Resource-Efficient Few-Shot Plant Disease Classification”) to manufacturing (e.g., “Few-Shot VLM-Based G-Code and HMI Verification in CNC Machining”).

Looking ahead, the focus will likely remain on enhancing the generalizability and robustness of FSL models, especially in cross-domain and real-world noisy environments. The exploration of hybrid human-AI systems, as seen in “Complementary Learning Approach for Text Classification using Large Language Models”, will also grow, integrating human expertise for more reliable and explainable outcomes. As models become more data-efficient and adaptable, few-shot learning is not just an incremental improvement; it’s a paradigm shift that will democratize AI, making powerful capabilities available even to those with limited data resources. The future of AI is undoubtedly few-shot.