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

Latest 50 papers on few-shot learning: Dec. 27, 2025

The world of AI and Machine Learning is constantly evolving, driven by the insatiable demand for intelligent systems that can learn and adapt efficiently. At the heart of this evolution lies few-shot learning, a paradigm-shifting approach enabling models to generalize from a handful of examples, dramatically reducing the need for vast, expensive datasets. This is a critical area of interest, especially as we push AI into domains with inherent data scarcity, from specialized medical diagnostics to complex robotic tasks. Recent breakthroughs, as showcased in a flurry of innovative research, are redefining what’s possible with limited data.

The Big Idea(s) & Core Innovations

Many of the recent advancements converge on a central theme: how to squeeze maximum knowledge from minimal data, often by leveraging foundational models and smart data handling. One compelling approach, explored by Bornschein et al. from Google DeepMind in their paper, “Fine-Tuned In-Context Learners for Efficient Adaptation”, proposes a unified strategy combining fine-tuning with in-context learning for LLMs. This ICL+FT method demonstrates superior performance in data-scarce scenarios, showing that the synergy between these techniques can be more potent than either alone. Similarly, Zheng et al. from the Institute of Neuroscience, Chinese Academy of Sciences delve into the mechanics of in-context learning, revealing the importance of “Label Words as Local Task Vectors in In-Context Learning”. This local task vector concept explains how LLMs encode rule-based information in categorization, allowing for few-shot performance with ‘dummy inputs.’

Beyond LLMs, few-shot learning is making strides across diverse domains. In medical imaging, the “SSL-MedSAM2: A Semi-supervised Medical Image Segmentation Framework Powered by Few-shot Learning of SAM2” by Gong and Chen from University of Nottingham utilizes the Segment Anything Model 2 (SAM2) to generate high-quality pseudo-labels without user prompts, significantly cutting down on annotation costs. This aligns with the privacy-preserving Federated Few-Shot Learning (FFSL) framework for epileptic seizure detection, introduced by Sysoykova et al. from Pro2Future GmbH, Linz, Austria, which enables personalized diagnosis without centralized data sharing—a critical innovation for healthcare AI.

In computer vision, the “ABounD: Adversarial Boundary-Driven Few-Shot Learning for Multi-Class Anomaly Detection” by Deng et al. from Nanjing University tackles anomaly detection with minimal data by dynamically adapting to class-specific variations. Furthermore, “Domain-RAG: Retrieval-Guided Compositional Image Generation for Cross-Domain Few-Shot Object Detection” by Li et al. from Fudan University introduces a training-free image generation framework that creates domain-consistent synthetic data, solving a major challenge in cross-domain detection. Even traditional deep learning models are getting a few-shot makeover; for instance, Author One et al.’s “Few-Shot Learning of a Graph-Based Neural Network Model Without Backpropagation” from University of Example offers a graph-based approach to bypass backpropagation entirely for knowledge transfer in low-data settings.

Other notable innovations include “TS-HINT: Enhancing Semiconductor Time Series Regression Using Attention Hints From Large Language Model Reasoning” by Rico et al. from SUTD, Singapore, which uses LLM-based reasoning and attention mechanisms to improve semiconductor manufacturing predictions. Zhang et al. from Huazhong University of Science and Technology address an important template bias in CLIP models with “Decoupling Template Bias in CLIP: Harnessing Empty Prompts for Enhanced Few-Shot Learning”, significantly boosting performance and robustness.

Under the Hood: Models, Datasets, & Benchmarks

These advancements are often powered by novel architectures, specially crafted datasets, and robust evaluation benchmarks:

• FSL-HDnn: A 40 nm on-device learning accelerator integrating feature extraction with hyperdimensional computing for energy-efficient few-shot learning at the edge. (FSL-HDnn: A 40 nm Few-shot On-Device Learning Accelerator with Integrated Feature Extraction and Hyperdimensional Computing)
• DeLTa: Combines LLMs with decision trees for tabular data prediction, avoiding data serialization. (LLM Meeting Decision Trees on Tabular Data)
• DendSN & DendSNN: A novel spiking neuron model incorporating dendritic morphology, demonstrating superior performance in classification and few-shot learning. (Scalable Dendritic Modeling Advances Expressive and Robust Deep Spiking Neural Networks, Code: https://github.com/PKU-SPIN/DendSNN)
• CoCa & LoRA: Multimodal foundation models adapted for few-shot learning, highlighting the dual role of data augmentation and the benefits of Supervised Contrastive (SupCon) loss. (Adapting Multimodal Foundation Models for Few-Shot Learning: A Comprehensive Study on Contrastive Captioners)
• FSL-HDnn from University of California, Berkeley introduces an on-device learning accelerator that merges feature extraction with hyperdimensional computing, enabling efficient few-shot learning at the edge.
• SEAM (Semantic Assembly Representation): A new representation for VLM-based robot manipulation, balancing VLM-comprehensibility and action-generalizability. (Rethinking Intermediate Representation for VLM-based Robot Manipulation)
• PHSD dataset: A large-scale human-humanoid dataset for egocentric manipulation, enabling models like Human0 to exhibit language following and few-shot learning. (In-N-On: Scaling Egocentric Manipulation with in-the-wild and on-task Data)
• ARQUSUMM: Leverages LLM in-context learning grounded in argumentation theory for argument-aware quantitative summarization of online conversations. (ARQUSUMM: Argument-aware Quantitative Summarization of Online Conversations, Code: https://github.com/antangrocket1312/ArQuSumm)
• VarCon: A variational supervised contrastive learning framework that achieves state-of-the-art performance with fewer epochs and smaller batch sizes. (Variational Supervised Contrastive Learning, Code: https://github.com/ziwenwang28/VarContrast)
• BanglaSentNet: An explainable hybrid deep learning framework for multi-aspect sentiment analysis in Bangla e-commerce reviews, achieving high accuracy with few-shot capabilities. (BanglaSentNet: An Explainable Hybrid Deep Learning Framework for Multi-Aspect Sentiment Analysis with Cross-Domain Transfer Learning)
• FFSL framework: Combines federated learning and few-shot learning for EEG-based seizure detection on non-IID datasets, preserving privacy. (Federated Few-Shot Learning for Epileptic Seizure Detection Under Privacy Constraints)
• SEAN TOGETHER dataset (v2): An augmented dataset with real-world interaction data used for predicting human perceptions of robot performance. (Few-Shot Inference of Human Perceptions of Robot Performance in Social Navigation Scenarios)

Impact & The Road Ahead

The impact of these advancements is profound and far-reaching. Few-shot learning is not just an academic curiosity; it’s a practical necessity for democratizing AI, making sophisticated models accessible in environments where data collection is difficult, expensive, or privacy-sensitive. From enabling resource-efficient plant disease classification (as seen in J. Sun et al.’s work from University of Agriculture, Japan) to revolutionizing semiconductor time series regression and wireless spectrum management with foundation models like SpectrumFM ([Chunyu Liu, University of California, Berkeley]), these innovations are pushing AI into critical industrial and scientific domains.

The integration of LLMs with specialized tasks, whether for systematic review screening ([Liu et al., Duke University]) or code refactoring (Author A et al.), highlights their versatility even in low-shot settings. The development of curvature-aware safety restoration in LLMs ([Bach et al., Deakin University]) ensures that fine-tuning doesn’t compromise safety, a crucial step for real-world deployments. Moreover, the focus on on-device continual error correction ([Paramonov et al., Samsung R&D Institute UK]) exemplifies a shift towards more adaptive and user-centric AI systems.

The road ahead for few-shot learning promises even greater breakthroughs. We can anticipate more robust multimodal models that can interpret and generate across modalities with even fewer examples, stronger privacy-preserving methods, and specialized hardware accelerators that bring advanced AI capabilities directly to the edge. As researchers continue to refine techniques like contrastive learning, architectural innovations, and advanced prompt engineering, few-shot learning will undoubtedly unlock new frontiers for AI, making intelligent systems more adaptable, efficient, and impactful across every sector.