| SgVA-CLIP |
- Accuracy
- 98.72
|
SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for Few-shot Image Classification 2022-11-28 |
코드 1 |
| CAML [Laion-2b] |
- Accuracy
- 98.6
|
Context-Aware Meta-Learning 2023-10-17 |
코드 1 |
| P>M>F (P=DINO-ViT-base, M=ProtoNet) |
- Accuracy
- 98.4
|
Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference 2022-04-15 |
코드 1 |
| TRIDENT |
- Accuracy
- 95.95
|
Transductive Decoupled Variational Inference for Few-Shot Classification 2022-08-22 |
코드 1 |
| BAVARDAGE |
- Accuracy
- 91.65
|
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification 2022-09-18 |
연결 없음 |
| PEMnE-BMS*(transductive) |
- Accuracy
- 91.53
|
Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning 2021-10-18 |
코드 1 |
| Transductive CNAPS + FETI |
- Accuracy
- 91.5
|
Enhancing Few-Shot Image Classification with Unlabelled Examples 2020-06-17 |
코드 1 · 코드 2 |
| PT+MAP+SF+SOT (transductive) |
- Accuracy
- 91.34
|
The Self-Optimal-Transport Feature Transform 2022-04-06 |
코드 1 |
| PT+MAP+SF+BPA (transductive) |
- Accuracy
- 91.34
|
The Balanced-Pairwise-Affinities Feature Transform 2024-06-25 |
코드 1 |
| AmdimNet |
- Accuracy
- 90.98
|
Self-Supervised Learning For Few-Shot Image Classification 2019-11-14 |
코드 1 · 코드 2 |
| Simple CNAPS + FETI |
- Accuracy
- 90.3
|
Improved Few-Shot Visual Classification 2019-12-07 |
코드 1 · 코드 2 |
| HCTransformers |
- Accuracy
- 89.19
|
Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learning 2022-03-17 |
코드 1 |
| Illumination Augmentation |
- Accuracy
- 89.14
|
Sill-Net: Feature Augmentation with Separated Illumination Representation 2021-02-06 |
코드 1 |
| EASY 3xResNet12 (transductive) |
- Accuracy
- 89.14
|
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients 2022-01-24 |
코드 1 · 코드 2 · 코드 3 |
| PT+MAP |
- Accuracy
- 88.82
|
Leveraging the Feature Distribution in Transfer-based Few-Shot Learning 2020-06-06 |
코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 · 코드 6 |
| EASY 2xResNet12 1/√2 (transductive) |
- Accuracy
- 88.57
|
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients 2022-01-24 |
코드 1 · 코드 2 · 코드 3 |
| EPNet + SSL |
- Accuracy
- 88.05
|
Embedding Propagation: Smoother Manifold for Few-Shot Classification 2020-03-09 |
코드 1 |
| EASY 3xResNet12 (inductive) |
- Accuracy
- 87.15
|
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients 2022-01-24 |
코드 1 · 코드 2 · 코드 3 |
| CPEA |
- Accuracy
- 87.06
|
Class-Aware Patch Embedding Adaptation for Few-Shot Image Classification 2023-01-01 |
코드 1 |
| SemFew-Trans |
- Accuracy
- 86.49
|
Simple Semantic-Aided Few-Shot Learning 2023-11-30 |
코드 1 |
| FewTURE |
- Accuracy
- 86.38
|
Rethinking Generalization in Few-Shot Classification 2022-06-15 |
코드 1 |
| EASY 2xResNet12 1/√2 (inductive) |
- Accuracy
- 86.28
|
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients 2022-01-24 |
코드 1 · 코드 2 · 코드 3 |
| COSOC (inductive) |
- Accuracy
- 85.16
|
Rectifying the Shortcut Learning of Background for Few-Shot Learning 2021-07-16 |
코드 1 |
| Invariance-Equivariance |
- Accuracy
- 84.78
|
Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning 2021-03-01 |
코드 1 |
| LaplacianShot |
- Accuracy
- 84.72
|
Laplacian Regularized Few-Shot Learning 2020-06-29 |
코드 1 · 코드 2 |
| PEMbE-NCM (inductive) |
- Accuracy
- 84.67
|
Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning 2021-10-18 |
코드 1 |
| MSENet |
- Accuracy
- 84.42
|
Enhancing Few-Shot Image Classification through Learnable Multi-Scale Embedding and Attention Mechanisms 2024-09-12 |
코드 1 |
| BD-CSPN + ESFR (WRN) |
- Accuracy
- 84.36
|
Unsupervised Embedding Adaptation via Early-Stage Feature Reconstruction for Few-Shot Classification 2021-06-22 |
코드 1 |
| EPNet |
- Accuracy
- 84.34
|
Embedding Propagation: Smoother Manifold for Few-Shot Classification 2020-03-09 |
코드 1 |
| MetaQDA |
- Accuracy
- 84.28
|
Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition 2021-01-08 |
코드 1 · 코드 2 |