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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MAML-KT: Addressing Cold Start Problem in Knowledge Tracing for New Students via Few-Shot Model-Agnostic Meta Learning
Feb 24, 2026Indronil Bhattacharjee, Christabel WayllaceKnowledge Tracing Few-Shot Learning
New Mexico State University, Las Cruces, New Mexico, USA
Adaptive Data Augmentation with Multi-armed Bandit: Sample-Efficient Embedding Calibration for Implicit Pattern Recognition
Feb 22, 2026Minxue Tang, Yangyang Yu, Aolin Ding +3Few-Shot Learning Data Augmentation
Duke University · Center for Advanced AI, Accenture
Active perception and disentangled representations allow continual, episodic zero and few-shot learning
Feb 22, 2026David Rawlinson, Gideon KowadloContinual Learning Few-Shot Learning
Cerenaut AI Melbourne Australia
Prompt Tuning for CLIP on the Pretrained Manifold
Feb 22, 2026Xi Yang, Yuanrong Xu, Weigang Zhang +3VLM Adaptation Visual Prompt Learning
Guizhou University, Guiyang, China · Harbin Institute of Technology, China · The Chinese University of Hong Kong, Shenzhen, China
Structure-Level Disentangled Diffusion for Few-Shot Chinese Font Generation
Feb 21, 2026Jie Li, Suorong Yang, Jian Zhao +1Few-Shot Learning Conditional Diffusion Models
State Key Laboratory for Novel Software Technology, Nanjing University, China · School of Artificial Intelligence, Nanjing University, China · Department of Computer Science and Technology, Nanjing University, China +1
Spatio-temporal Decoupled Knowledge Compensator for Few-Shot Action Recognition
Feb 20, 2026Hongyu Qu, Xiangbo Shu, Rui Yan +3Video Action Recognition Few-Shot Learning
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China. · State Key Lab of Brain-Machine Intelligence, Zhejiang University, China.
A Few-Shot LLM Framework for Extreme Day Classification in Electricity Markets
Feb 17, 2026Saud Alghumayjan, Ming Yi, Bolun XuTime Series Classification Few-Shot Learning
Dept. of Earth and Environmental Engineering, Columbia University, New York, NY 10027, USA
Universal Algorithm-Implicit Learning
Feb 16, 2026Stefano Woerner, Seong Joon Oh, Christian F. BaumgartnerMeta-Learning Few-Shot Learning
University of Tübingen, Germany · KAIST, Korea · Faculty of Health Sciences and Medicine, University of Lucerne, Switzerland
CoDi -- an exemplar-conditioned diffusion model for low-shot counting
Dec 23, 2025Grega Šuštar, Jer Pelhan, Alan Lukežič +1Object Counting Few-Shot Learning
Faculty of Computer and Information Science, University of Ljubljana, Slovenia
Few-Shot Specific Emitter Identification via Integrated Complex Variational Mode Decomposition and Spatial Attention Transfer
Dec 18, 2025Chenyu Zhu, Zeyang Li, Ziyi Xie +1Few-Shot Learning
School of Information Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China · School of Electrical Engineering and Computer Science, and the Digital Futures, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden · Ranplan Wireless Network Design Ltd., Cambridge, CB23 3UY, U.K.
Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective
Dec 11, 2025Tian Liu, Anwesha Basu, James Caverlee +1VLM Adaptation Few-Shot Learning
Texas A&M University · University of Macau · Institute of Collaborative Innovation
Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples
Sep 30, 2025Shiyu Wu, Shuyan Li, Jing Li +2Few-Shot Learning AI-Generated Image Detection
Institute of Automation, Chinese Academy of Sciences, Beijing, China · Beijing Academy of Artificial Intelligence, Beijing, China · University of Chinese Academy of Sciences, Beijing, China +3
Effects of Structural Allocation of Geometric Task Diversity in Linear Meta-Learning Models
Sep 22, 2025Saptati Datta, Nicolas W. Hengartner, Yulia Pimonova +2Meta-Learning Few-Shot Learning
Department of Statistics, Texas A&M University, TX, USA · Los Alamos National Laboratory, Los Alamos, USA.
Through the Looking Glass: A Dual Perspective on Weakly-Supervised Few-Shot Segmentation
Aug 22, 2025Jiaqi Ma, Guo-Sen Xie, Fang Zhao +1Weakly Supervised Image Segmentation Weakly Supervised Semantic Segmentation
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China · School of Intelligence Science and Technology, Nanjing University, Suzhou 215163, China
Rational Inverse Reasoning: Few-Shot Imitation by Inferring Intent through Planning
Aug 12, 2025Ben Zandonati, Tomás Lozano-Pérez, Leslie Pack KaelblingRobot Skill Learning Generalization in Robotic Manipulation
MIT CSAIL
Context Tuning for In-Context Optimization
Jul 6, 2025Jack Lu, Ryan Teehan, Zhenbang Yang +1Few-Shot Learning In-Context Learning
Agentic Learning AI Lab, New York University
Enabling Validation for Robust Few-Shot Recognition
Jun 5, 2025Hanxin Wang, Tian Liu, Shu KongFew-Shot Learning Few-Shot Image Classification
University of Macau · Texas A&M University · Institute of Collaborative Innovation
Hierarchical Relation-augmented Representation Generalization for Few-shot Action Recognition
Apr 14, 2025Hongyu Qu, Ling Xing, Jiachao Zhang +3Temporal Reasoning Hierarchical Representation Learning
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China · Artificial Intelligence Industrial Technology Research Institute, Nanjing Institute of Technology, Nanjing 211167, China
DRESS: Disentangled Representation-based Self-Supervised Meta-Learning for Diverse Tasks
Mar 12, 2025Wei Cui, Tongzi Wu, Jesse C. Cresswell +2Disentangled Representation Learning Meta-Learning
Layer 6 AI, Toronto, Canada
PPFedIT: Towards Privacy-Preserving Federated Instruction Tuning with Few-shot Local Examples
Mar 10, 2024Zhuo Zhang, Jingyuan Zhang, Jintao Huang +6Privacy Leakage in Language Models Few-Shot Learning
Harbin Institute of Technology, Shenzhen, China · Peng Cheng Lab, Shenzhen, China · Kuaishou, Beijing, China +1
Structure-Guided Self-Supervised Matching for One-Shot Medical Landmark Detection
Mar 3, 2022Qingsong Yao, Zhen Huang, Ao Wang +4Self-Supervised Learning Image Matching
Soochow School of Artificial Intelligence Renmin University of China · Department of Computer Science University of Science and Technology of China · School of Biomedical Engineering University of Science and Technology of China +3

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MAML-KT: Addressing Cold Start Problem in Knowledge Tracing for New Students via Few-Shot Model-Agnostic Meta Learning

Indronil Bhattacharjee, Christabel Wayllace

New Mexico State University, Las Cruces, New Mexico, USA

Knowledge Tracing Few-Shot Learning Meta-Learning

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Knowledge tracing (KT) models are commonly evaluated by training on early interactions from all students and testing on later responses. While effective for measuring average predictive performance, this evaluation design obscures a cold start scenario that arises in deployment, where models must infer the knowledge state of previously unseen students from only a few initial interactions. Prior studies have shown that under this setting, standard empirically risk-minimized KT models such as DKT, DKVMN and SAKT exhibit substantially lower early accuracy than previously reported. We frame new-student performance prediction as a few-shot learning problem and introduce MAML-KT, a model-agnostic meta learning approach that learns an initialization optimized for rapid adaptation to new students using one or two gradient updates. We evaluate MAML-KT on ASSIST2009, ASSIST2015 and ASSIST2017 using a controlled cold start protocol that trains on a subset of students and tests on held-out learners across early interaction windows (questions 3-10 and 11-15), scaling cohort sizes from 10 to 50 students. Across datasets, MAML-KT achieves higher early accuracy than prior KT models in nearly all cold start conditions, with gains persisting as cohort size increases. On ASSIST2017, we observe a transient drop in early performance that coincides with many students encountering previously unseen skills. Further analysis suggests that these drops coincide with skill novelty rather than model instability, consistent with prior work on skill-level cold start. Overall, optimizing KT models for rapid adaptation reduces early prediction error for new students and provides a clearer lens for interpreting early accuracy fluctuations, distinguishing model limitations from genuine learning and knowledge acquisition dynamics.