Few-Shot Learning papers, 2023–2026

Key findings

• Few-Shot Learning fell from 251 papers in 2023 to 224 in 2026, so interest is cooling.
• CVPR publishes the most Few-Shot Learning work: 303 papers from 2023 to 2026, 34% of the four-venue total.
• The fastest-growing overlap is with Diffusion Models: papers tagged with both rose from 4 in 2023 to 24 in 2026.
• Among the 5 topics under Meta-Learning and Transfer, it ranks #4 by 2026 paper count; the fastest grower in the group, Foundation Model Adaptation, is up 3.8x versus 0.9x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026BAPM: Boundary-Aware Prompt Mining for Training-Free Few-Shot Medical Image Segmentation
• ICML 2026Bayes-inspired Integration of Pretrained Priors and Few-Shot Evidence for Few-Shot Classification
• ICLR 2026Adaptive Concept Discovery for Interpretable Few-Shot Text Classification
• CVPR 2026A Closer Look at Cross-Domain Few-Shot Object Detection Fine-Tuning Matters
• NeurIPS 2025A Few Moments Please: Scalable Graphon Learning via Moment Matching
• ICML 2025FedBEns: One-Shot Federated Learning based on Bayesian Ensemble
• ICLR 2025Dist Loss: Enhancing Regression in Few-Shot Region through Distribution Distance Constraint
• CVPR 20253D Prior Is All You Need: Cross-Task Few-shot 2D Gaze Estimation
• NeurIPS 2024Few-shot Algorithms for Consistent Neural Decoding (FALCON) Benchmark
• ICML 2024Accelerating Convergence in Bayesian Few-Shot Classification
• ICLR 2024Few-shot Hybrid Domain Adaptation of Image Generator
• CVPR 2024Deformable One-shot Face Stylization via DINO Semantic Guidance
• NeurIPS 2023Alignment with human representations supports robust few-shot learning
• ICML 2023A Category-theoretical Meta-analysis of Definitions of Disentanglement
• ICLR 2023Bidirectional Language Models Are Also Few-shot Learners
• CVPR 2023A Meta-Learning Approach to Predicting Performance and Data Requirements
• NeurIPS 2026Beyond Normal References: Discriminative Few-Shot Anomaly Detection
• ICML 2026BiCrossNet with Decoupled Dual Generators: A Parameter‑Efficient and Generalizable Few‑Shot Custom Gesture Recognition Framework
• ICLR 2026Dual Distillation for Few-Shot Anomaly Detection
• CVPR 2026Beyond Patches Global-aware Autoregressive Model for Multimodal Few-Shot Font Generation

All Few-Shot Learning papers

203 papers, page 3 of 5

101. ICML 2025Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation
102. ICML 2025Adaptive Multi-prompt Contrastive Network for Few-shot Out-of-distribution Detection
103. ICML 2025Alpha-SQL: Zero-Shot Text-to-SQL using Monte Carlo Tree Search
104. ICML 2025Bootstrapping Self-Improvement of Language Model Programs for Zero-Shot Schema Matching
105. ICML 2025CERTAIN: Context Uncertainty-aware One-Shot Adaptation for Context-based Offline Meta Reinforcement Learning
106. ICML 2025Does One-shot Give the Best Shot? Mitigating Model Inconsistency in One-shot Federated Learning
107. ICML 2025DyPolySeg: Taylor Series-Inspired Dynamic Polynomial Fitting Network for Few-shot Point Cloud Semantic Segmentation
108. ICML 2025FedBEns: One-Shot Federated Learning based on Bayesian Ensemble
109. ICML 2025Federated Oriented Learning: A Practical One-Shot Personalized Federated Learning Framework
110. ICML 2025Feedforward Few-shot Species Range Estimation
111. ICML 2025Few-Shot Learner Generalizes Across AI-Generated Image Detection
112. ICML 2025FSTLLM: Spatio-Temporal LLM for Few Shot Time Series Forecasting
113. ICML 2025Geometric Feature Embedding for Effective 3D Few-Shot Class Incremental Learning
114. ICML 2025GuidedQuant: Large Language Model Quantization via Exploiting End Loss Guidance
115. ICML 2025MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning
116. ICML 2025On the Provable Separation of Scales in Maximal Update Parameterization
117. ICML 2025One-Shot Heterogeneous Federated Learning with Local Model-Guided Diffusion Models
118. ICML 2025PCEvolve: Private Contrastive Evolution for Synthetic Dataset Generation via Few-Shot Private Data and Generative APIs
119. ICML 2025Pessimism Principle Can Be Effective: Towards a Framework for Zero-Shot Transfer Reinforcement Learning
120. ICML 2025Position: Don't Use the CLT in LLM Evals With Fewer Than a Few Hundred Datapoints
121. ICML 2025Provable Zero-Shot Generalization in Offline Reinforcement Learning
122. ICML 2025Provably Improving Generalization of Few-shot models with Synthetic Data
123. ICML 2025Quantifying Prediction Consistency Under Fine-tuning Multiplicity in Tabular LLMs
124. ICML 2025Random Registers for Cross-Domain Few-Shot Learning
125. ICML 2025Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning
126. ICML 2025Revisiting Neural Networks for Few-Shot Learning: A Zero-Cost NAS Perspective
127. ICML 2025SAND: One-Shot Feature Selection with Additive Noise Distortion
128. ICML 2025Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning
129. ICML 2025Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation
130. ICML 2025SLiM: One-shot Quantization and Sparsity with Low-rank Approximation for LLM Weight Compression
131. ICML 2025The Missing Alignment Link of In-context Learning on Sequences
132. ICML 2025The Surprising Effectiveness of Test-Time Training for Few-Shot Learning
133. ICML 2025Unlocking the Power of SAM 2 for Few-Shot Segmentation
134. ICML 2025Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer
135. ICML 2025Zero-Shot Offline Imitation Learning via Optimal Transport
136. NeurIPS 2025A Few Moments Please: Scalable Graphon Learning via Moment Matching
137. NeurIPS 2025An Effective Levelling Paradigm for Unlabeled Scenarios
138. NeurIPS 2025Attack via Overfitting: 10-shot Benign Fine-tuning to Jailbreak LLMs
139. NeurIPS 2025Blackbox Model Provenance via Palimpsestic Membership Inference
140. NeurIPS 2025Dataset Distillation for Pre-Trained Self-Supervised Vision Models
141. NeurIPS 2025DETree: DEtecting Human-AI Collaborative Texts via Tree-Structured Hierarchical Representation Learning
142. NeurIPS 2025Diffusion-Classifier Synergy: Reward-Aligned Learning via Mutual Boosting Loop for FSCIL
143. NeurIPS 2025Domain-Specific Pruning of Large Mixture-of-Experts Models with Few-shot Demonstrations
144. NeurIPS 2025DON’T NEED RETRAINING: A Mixture of DETR and Vision Foundation Models for Cross-Domain Few-Shot Object Detection
145. NeurIPS 2025Dual Alignment Framework for Few-shot Learning with Inter-Set and Intra-Set Shifts
146. NeurIPS 2025Enhancing Zero-Shot Black-Box Optimization via Pretrained Models with Efficient Population Modeling, Interaction, and Stable Gradient Approximation
147. NeurIPS 2025Evolving and Regularizing Meta-Environment Learner for Fine-Grained Few-Shot Class-Incremental Learning
148. NeurIPS 2025Fast exact recovery of noisy matrix from few entries: the infinity norm approach
149. NeurIPS 2025Few-Shot Knowledge Distillation of LLMs With Counterfactual Explanations
150. NeurIPS 2025Few-Shot Learning from Gigapixel Images via Hierarchical Vision-Language Alignment and Modeling

FAQ

How many Few-Shot Learning papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

898 in total: 251 in 2023, 220 in 2024, 203 in 2025, 224 in 2026.

How many Few-Shot Learning papers are at NeurIPS 2026?

NeurIPS 2026 has 51 Few-Shot Learning papers, versus 68 the year before.

How many Few-Shot Learning papers are at ICLR 2026?

ICLR 2026 has 38 Few-Shot Learning papers, versus 36 the year before.

How many Few-Shot Learning papers are at ICML 2026?

ICML 2026 has 56 Few-Shot Learning papers, versus 35 the year before.

How many Few-Shot Learning papers are at CVPR 2026?

CVPR 2026 has 79 Few-Shot Learning papers, versus 64 the year before.

Which conference publishes the most Few-Shot Learning papers?

CVPR, with 303 papers from 2023 to 2026.