Research map / Label-efficient and robust learning
Zero-/few-shot and meta-learning: research map
784 accepted papers on Zero-/few-shot and meta-learning in Label-efficient and robust learning, from ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026), grouped into 3 clusters and 11 approaches. The busiest year so far is 2023.
Within Label-efficient and robust learning, its share shrank from 7.2% in 2023–24 to 5.8% in 2025–26 (206 → 176 papers at ICML, NeurIPS, CVPR and AAAI, the venues with data for all four years).
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Approaches and key papers
zero shot · zsl · seen · 400 papers
Approaches in this cluster:
- Generative feature synthesis for zero-shot (117 papers)
Synthesizes unseen-class features and aligns semantic embeddings to reduce seen-class bias. - Zero-shot robustness and transfer (101 papers)
Studies and improves generalization of foundation models to new groups, domains and data. - Robust fine-tuning of zero-shot models (92 papers)
Improves robustness and prompt ensembles of zero-shot models, with theory for zero-shot prediction. - Transductive and semantic-dictionary zero-shot (90 papers)
Uses unlabeled test data and learned semantic dictionaries for zero-shot and CLIP transfer.
Most cited and most cited since 2024:
- Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data (CVPR 2024 · 1,178 citations)
- Feature Generating Networks for Zero-Shot Learning (CVPR 2018 · 1,080 citations)
- FoundationStereo: Zero-Shot Stereo Matching (CVPR 2025 · 98 citations)
- Telling Left from Right: Identifying Geometry-Aware Semantic Correspondence (CVPR 2024 · 48 citations)
fsl · shot classification · support · 235 papers
Approaches in this cluster:
- Embedding adaptation for few-shot classification (71 papers)
Adapts feature embeddings with set functions and feature-wise transforms, including cross-domain settings. - Fine-tuning strategies for few-shot (67 papers)
Revisits partial fine-tuning, auxiliary data and causal interventions for few-shot generalization. - Fine-grained and cross-domain few-shot baselines (59 papers)
Uses baselines, feature reconstruction and prototypical refinement for fine-grained or cross-domain few-shot tasks. - Alignment-based few-shot metric learning (38 papers)
Learns dynamic alignment, affiliation and task-tailored embeddings between support and query samples.
Most cited and most cited since 2024:
- Prototypical Networks for Few-shot Learning (NeurIPS 2017 · 5,178 citations)
- Dynamic Few-Shot Visual Learning Without Forgetting (CVPR 2018 · 1,151 citations)
- Cross-Layer and Cross-Sample Feature Optimization Network for Few-Shot Fine-Grained Image Classification (AAAI 2024 · 55 citations)
- APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentation (CVPR 2024 · 34 citations)
maml · meta algorithms · meta meta · 149 papers
Approaches in this cluster:
- Meta-augmentation and meta-objectives (60 papers)
Improves meta-learning generalization through task augmentation, contrastive objectives and target-model transfer. - Meta-learning analysis and benchmarks (50 papers)
Analyzes meta-learned representations and builds benchmarks and Bayesian or convex-optimization learners. - Normalization and unsupervised meta-learning (39 papers)
Adapts batch normalization for tasks and builds unsupervised or hard-sample meta-learning.
Most cited and most cited since 2024:
- Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks (ICML 2017 · 6,288 citations)
- Meta-Learning With Differentiable Convex Optimization (CVPR 2019 · 1,317 citations)
- FREE: Faster and Better Data-Free Meta-Learning (CVPR 2024 · 5 citations)
- Improving Generalization via Meta-Learning on Hard Samples (CVPR 2024 · 4 citations)
Related topics in Label-efficient and robust learning
- Noisy labels (462)
- Test-time adaptation (359)
- OOD and anomaly detection (614)
- Recognition and long-tail (4,953)
- Semi-supervised learning (623)
- Self-supervised and contrastive (988)
- Domain adaptation and generalization (1,330)
- Knowledge distillation (544)