Few-Shot Learning

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33 papers in the last four weeks, up 94% on the four weeks before. 0.2% of all new papers.

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Do You Even Need More Data? Rethinking LLM Deployment for Label-Scarce Tabular Classification with TabLens
May 23, 2026Daria Grushina, Kseniia Kuvshinova, Alina Kostromina +3Zero-Shot Learning LLM Evaluation
Sb AI Lab, HSE University, NES, Russia · MBZUAI, United Arab Emirates · Sb AI Lab, HSE University, Russia +1
Advancing Graph Few-Shot Learning via In-Context Learning
May 23, 2026Renchu Guan, Yajun Wang, Chunli Guo +5Meta-Learning Few-Shot Learning
Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education
Beyond Normal References: Discriminative Few-Shot Anomaly Detection
May 22, 2026Huan Wang, Jun Shen, Jun Yan +1Few-Shot Anomaly Detection Few-Shot Learning
Singapore Management University, Singapore · University of Wollongong, Australia
Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation
May 19, 2026Junyuan Ma, Xunzhi Xiang, Wenbin Li +2Vision Foundation Model Adaptation Few-Shot Learning
1Nanjing University
Easier to Judge than to Find: Predicting In-Context Learning Success for Demonstration Selection
May 18, 2026Haochun Wang, Chaofen Yang, Jiatong Liu +5LLM Inference Efficiency In-Context Example Selection
Research Center for Social Computing and Interactive Robotics, Harbin Institute of Technology, China.
PERL: Parameter Efficient Reasoning in CLIP Latent Space
May 18, 2026Simone Carnemolla, Salvatore Calcagno, Daniela Giordano +2VLM Adaptation Few-Shot Learning
University of Catania
Few-Shot Network Intrusion Detection Using Online Triplet Mining
May 17, 2026Jack Wilkie, Hanan Hindy, Christos Tachtatzis +2Network Intrusion Detection Few-Shot Learning
Department of Electronics and Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UK · Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt · Faculty of Electrical Engineering, Czech Technical University, 160 00 Prague, Czech Republic
CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts
May 15, 2026Peiyuan Li, Yongqi Huang, Jitao Zhao +3Heterogeneous GNNs Few-Shot Learning
School of Computer Science and Technology, Tianjin University, Tianjin, China · Department of Health Technology and Informatics, and Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Neutral-Reference Prompting for Vision-Language Models
May 15, 2026Senmao Tian, Xiang Wei, Shunli ZhangVision-Language Models VLM Adaptation
Beijing Jiaotong University, Beijing, China.
Rethinking the Good Enough Embedding for Easy Few-Shot Learning
May 13, 2026Michael Karnes, Alper YilmazVisual Representation Learning Few-Shot Learning
The Ohio State University, Columbus, OH 43210, USA
SpurAudio: A Benchmark for Studying Shortcut Learning in Few-Shot Audio Classification
May 13, 2026Giries Abu Ayoub, Morad Tukan, Loay MualemFew-Shot Audio Classification Audio Understanding
Independent Researcher · University of Stuttgart, Germany · IMPRS-IS, Germany
STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition
May 13, 2026Hongli Liu, Yu Wang, Shengjie ZhaoCross-Modal Representation Learning Few-Shot Learning
School of Computer Science and Technology, Tongji University, Shanghai, China · Engineering Research Center of Key Software Technologies for Smart City Perception and Planning, Ministry of Education
GenCape: Structure-Inductive Generative Modeling for Category-Agnostic Pose Estimation
May 13, 2026Jiyong Rao, Yu Wang, Shengjie ZhaoFew-Shot Learning
School of Computer Science and Technology, Tongji University
Scaling few-shot spoken word classification with generative meta-continual learning
May 13, 2026Louise Beyers, Batsirayi Mupamhi Ziki, Ruan van der MerweFew-Shot Audio Classification Continual Learning
Bytefuse
Unlocking Compositional Generalization in Continual Few-Shot Learning
May 12, 2026Phu-Quy Nguyen-Lam, Phu-Hoa Pham, Dao Sy Duy Minh +3Object-Centric Representation Learning Few-Shot Learning
Faculty of Information Technology, University of Science, Vietnam National University, Ho Chi Minh City, Vietnam. · Department of Computer Science, University of Warwick, Coventry, United Kingdom.
Reviving In-domain Fine-tuning Methods for Source-Free Cross-domain Few-shot Learning
May 12, 2026Yaze Zhao, Yicong Liu, Yixiong Zou +2Source-Free Domain Adaptation Few-Shot Domain Adaptation
Medical Incident Causal Factors and Preventive Measures Generation Using Tag-based Example Selection in Few-shot Learning
May 11, 2026Yuna Haseyama, Tomoki Ito, Hiroki Sakaji +1Healthcare Few-Shot Learning
Graduate School of Information Science and Technology Hokkaido University Hokkaido, Japan · National Institute of Information and Communications Technology (NICT) Kyoto, Japan · Faculty of Information Science and Technology Hokkaido University Hokkaido, Japan
Empowering VLMs for Few-Shot Multimodal Time Series Classification via Tailored Agentic Reasoning
May 10, 2026Lin Li, Jiawei Huang, Qihao Quan +7Multivariate Time Series Classification Time Series Classification
Sun Yat-sen University Zhuhai, Guangdong, China · Xiaomi Corporation Beijing, China · University of Science and Technology of China Hefei, Anhui, China +1
SeBA: Semi-supervised few-shot learning via Separated-at-Birth Alignment for tabular data
May 8, 2026Kacper Jurek, Wojciech Batko, Marek Śmieja +1Multi-View Learning Few-Shot Learning
Jagiellonian University, Faculty of Mathematics and Computer Science · AGH University of Krakow
Quality-Conditioned Agreement in Automated Short Answer Scoring: Mid-Range Degradation and the Impact of Task-Specific Adaptation
May 8, 2026Abigail Victoria Gurin Schleifer, Moriah Ariely, Beata Beigman Klebanov +2LLM Evaluation AI in Education
Weizmann Institute of Science, Rehovot, Israel · ETS, Princeton, USA
In-Context Fixation: When Demonstrated Labels Override Semantics in Few-Shot Classification
May 8, 2026Ming LiuLLM Interpretability Few-Shot Learning
Amazon
Improving Graph Few-shot Learning with Hyperbolic Space and Denoising Diffusion
Apr 30, 2026Yonghao Liu, Jialu Sun, Wei Pang +4Few-Shot Learning
Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, College of Computer Science and Technology, Jilin University · School of Mathematical and Computer Sciences, Heriot-Watt University · Department of Information Engineering and Computer Science, University of Trento
Improving Diversity in Black-box Few-shot Knowledge Distillation
Apr 28, 2026Tri-Nhan Vo, Dang Nguyen, Kien Do +1Synthetic Data Augmentation Few-Shot Learning
Applied Artificial Intelligence Institute (A2I2), Deakin University, Australia
GEGLU-Transformer for IMU-to-EMG Estimation with Few-Shot Adaptation
Apr 28, 2026Miroljub Mihailovic, Luca Tonin, Stefano Tortora +1Transformer Wearable Sensing
Department of Information Engineering, University of Padua, 35131 Padua, Italy
Few-Shot Cross-Device Transfer for Quantum Noise Modeling on Real Hardware
Apr 27, 2026Sahil Al Farib, Sheikh Redwanul Islam, Azizur Rahman AnikFew-Shot Learning Cross-Domain Transfer Learning
United International University · Dhaka, Bangladesh
EV-CLIP: Efficient Visual Prompt Adaptation for CLIP in Few-shot Action Recognition under Visual Challenges
Apr 24, 2026Hyo Jin Jon, Longbin Jin, Eun Yi KimVLM Adaptation Domain Generalization
Computer Vision Lab., Konkuk University, Seoul, South Korea
One Shot Learning for Edge Detection on Point Clouds
Apr 24, 2026Zhikun Tu, Yuhe Zhang, Yiou Jia +2Point Cloud Learning Few-Shot Learning
School of Information Science and Technology, Northwest University, Xi’an, China · Department of Computer Science, Tel Aviv University, Tel Aviv, Israel
RADS: Reinforcement Learning-Based Sample Selection Improves Transfer Learning in Low-resource and Imbalanced Clinical Settings
Apr 22, 2026Wei Han, David Martinez, Anna Khanina +2Class-Imbalanced Learning Few-Shot Learning
School of Computing Technologies, RMIT University · Department of Infectious Disease, Peter MacCallum Cancer Centre · 3National Centre for Infections in Cancer, Melbourne +2
From Skeletons to Pixels: Few-Shot Precise Event Spotting via Representation and Prediction Distillation
Apr 21, 2026Zhong Han Ervin Yeoh, Jiang KanCross-Modal Knowledge Distillation Few-Shot Learning
Department of Business Analytics School of Computing National University of Singapore · Department of Computer Science School of Computing National University of Singapore
HEALing Entropy Collapse: Enhancing Exploration in Few-Shot RLVR via Hybrid-Domain Entropy Dynamics Alignment
Apr 20, 2026Zhanyu Liu, Qingguo Hu, Ante Wang +5RL for Language Model Reasoning Few-Shot Learning
Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan (Xiamen University), Ministry of Culture and Tourism, China · Tsinghua University · Xiamen Unisound Intelligence Technology Co., Ltd

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Do You Even Need More Data? Rethinking LLM Deployment for Label-Scarce Tabular Classification with TabLens

Daria Grushina, Kseniia Kuvshinova, Alina Kostromina, Aziz Temirkhanov+2

Sb AI Lab, HSE University, NES, Russia · MBZUAI, United Arab Emirates · Sb AI Lab, HSE University, Russia+1

Zero-Shot Learning LLM Evaluation Tabular Classification Few-Shot Learning Tabular ICL

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Every tabular classification task requires sufficient labeled data. When labels are scarce, practitioners can collect more examples, encode domain expertise into features, or accept lower performance. Large language models offer an alternative by leveraging knowledge acquired during pretraining. This raises three questions: (i) can zero-shot inference reduce the need for labeled data; (ii) can a richer prompt reduce the need for more examples; and (iii) can encoded domain knowledge compensate for missing data? To answer each under explicit, testable conditions, we introduce TabLens, a benchmark of 91 real-world and synthetic datasets across eight domains, plus eight industrial datasets with free-text columns. First, below a complexity threshold estimable from a dataset's intrinsic dimensionality before running an LLM, zero-shot inference matches the accuracy of up to about 16 labeled examples. This holds whether the model generates an answer or, more cheaply, scores candidate labels by log-probabilities, which loses almost no accuracy except for the smallest models. Above the threshold, the advantage disappears. On datasets with free-text columns, however, zero-shot LLMs outperform 16-shot TabPFN-3 Plus without manual feature engineering. Second, a richer prompt reduces the number of required demonstrations only when it includes both real column names and a plain-language task description; providing only one can be worse than neither, particularly at higher shot counts. Third, on LLM-synthetic datasets, explicit if--then expert rules help only when the prompt provides enough task context to interpret them, and even then, they substitute for at most about 16 real examples. Together, these results establish testable conditions under which LLMs can reduce data-collection and feature-engineering requirements for tabular classification.