基础模型适配论文趋势 2023–2026

主要结论

• 基础模型适配论文从 2023 年的 159 篇增长到 2026 年的 602 篇，三年间扩大到约 3.8 倍。
• NeurIPS 是发基础模型适配论文最多的会议，2023–2026 年共 531 篇，占四个会议总量的 36%。
• 交叉方向里涨得最快的是深度学习 · foundation models：同时属于两个主题的论文从 2023 年的 7 篇增至 2026 年的 66 篇。
• 在「元学习与迁移」的 5 个主题里，它的 2026 年论文数排第 1；同组增长最快的持续学习是 1.7 倍，基础模型适配是 3.8 倍。

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

• NeurIPS 2026(Mis)generalization of Helpful-Only Fine-Tuning
• ICML 2026Active Learning with Foundation Model Priors: Efficient Learning under Class Imbalance
• ICLR 2026Benchmarking ECG Foundational Models: A Reality Check Across Clinical Tasks
• CVPR 2026CD-Buffer Complementary Dual-Buffer Framework for Test-Time Adaptation in Adverse Weather
• NeurIPS 2025An Investigation of Memorization Risk in Healthcare Foundation Models
• ICML 2025A Bayesian Model Selection Criterion for Selecting Pretraining Checkpoints
• ICLR 2025Better autoregressive regression with LLMs via regression-aware fine-tuning
• CVPR 20255%>100%: Breaking Performance Shackles of Full Fine-Tuning on Visual Recognition Tasks
• NeurIPS 2024Instruction Tuning Large Language Models to Understand Electronic Health Records
• ICML 2024By Tying Embeddings You Are Assuming the Distributional Hypothesis
• ICLR 2024A Hard-to-Beat Baseline for Training-free CLIP-based Adaptation
• CVPR 2024DePT: Decoupled Prompt Tuning
• NeurIPS 2023A Fast and Accurate Estimator for Large Scale Linear Model via Data Averaging
• ICML 2023Are Neurons Actually Collapsed? On the Fine-Grained Structure in Neural Representations
• ICLR 2023A Closer Look at Model Adaptation using Feature Distortion and Simplicity Bias
• CVPR 2023A-La-Carte Prompt Tuning (APT): Combining Distinct Data via Composable Prompting
• NeurIPS 2026A Graph Foundation Model for Unified Clustering
• ICML 2026AdaMeZO: Adam-style Zeroth-Order Optimizer for LLM Fine-tuning Without Maintaining the Moments
• ICLR 2026Bridging Radiology and Pathology Foundation Models via Concept-Based Multimodal Co-Adaptation
• CVPR 2026Fed-ADE Adaptive Learning Rate for Federated Post-adaptation under Distribution Shift

全部基础模型适配论文

共 24 篇，第 1 / 1 页

1. CVPR 2023A-La-Carte Prompt Tuning (APT): Combining Distinct Data via Composable Prompting
2. CVPR 2023Boosting Transductive Few-Shot Fine-Tuning With Margin-Based Uncertainty Weighting and Probability Regularization
3. CVPR 2023Divide and Adapt: Active Domain Adaptation via Customized Learning
4. CVPR 2023Fine-Grained Face Swapping via Regional GAN Inversion
5. CVPR 2023Foundation Model Drives Weakly Incremental Learning for Semantic Segmentation
6. CVPR 2023Guided Recommendation for Model Fine-Tuning
7. CVPR 2023Hint-Aug: Drawing Hints From Foundation Vision Transformers Towards Boosted Few-Shot Parameter-Efficient Tuning
8. CVPR 2023Integral Neural Networks
9. CVPR 2023Integrally Pre-Trained Transformer Pyramid Networks
10. CVPR 2023Masked Images Are Counterfactual Samples for Robust Fine-Tuning
11. CVPR 2023MELTR: Meta Loss Transformer for Learning To Fine-Tune Video Foundation Models
12. CVPR 2023MetaPortrait: Identity-Preserving Talking Head Generation With Fast Personalized Adaptation
13. CVPR 2023MHPL: Minimum Happy Points Learning for Active Source Free Domain Adaptation
14. CVPR 2023MOT: Masked Optimal Transport for Partial Domain Adaptation
15. CVPR 2023Neuro-Modulated Hebbian Learning for Fully Test-Time Adaptation
16. CVPR 2023Policy Adaptation From Foundation Model Feedback
17. CVPR 2023Prompt, Generate, Then Cache: Cascade of Foundation Models Makes Strong Few-Shot Learners
18. CVPR 2023Semi-Supervised Domain Adaptation With Source Label Adaptation
19. CVPR 2023Test Time Adaptation With Regularized Loss for Weakly Supervised Salient Object Detection
20. CVPR 2023Towards Better Stability and Adaptability: Improve Online Self-Training for Model Adaptation in Semantic Segmentation
21. CVPR 2023Towards Fast Adaptation of Pretrained Contrastive Models for Multi-Channel Video-Language Retrieval
22. CVPR 2023Train/Test-Time Adaptation With Retrieval
23. CVPR 2023Trainable Projected Gradient Method for Robust Fine-Tuning
24. CVPR 2023TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization

常见问题

2023–2026 年四大会议一共有多少篇基础模型适配论文？

共 1,492 篇，按年份分别是 2023 年 159 篇、2024 年 270 篇、2025 年 461 篇、2026 年 602 篇。

NeurIPS 2026 有多少篇基础模型适配论文？

NeurIPS 2026 有 182 篇基础模型适配论文，上一届是 174 篇。

ICLR 2026 有多少篇基础模型适配论文？

ICLR 2026 有 187 篇基础模型适配论文，上一届是 149 篇。

ICML 2026 有多少篇基础模型适配论文？

ICML 2026 有 142 篇基础模型适配论文，上一届是 76 篇。

CVPR 2026 有多少篇基础模型适配论文？

CVPR 2026 有 91 篇基础模型适配论文，上一届是 62 篇。

哪个会议的基础模型适配论文最多？

NeurIPS，2023–2026 年共 531 篇。