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SOTA
少样本图像分类
Few Shot Image Classification On Tiered
Few Shot Image Classification On Tiered
评估指标
Accuracy
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
Accuracy
Paper Title
Repository
CAML [Laion-2b]
96.8
Context-Aware Meta-Learning
TRIDENT
86.97
Transductive Decoupled Variational Inference for Few-Shot Classification
PEMnE-BMS*
86.07
Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning
PT+MAP
85.41
Leveraging the Feature Distribution in Transfer-based Few-Shot Learning
BAVARDAGE
85.20
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification
-
EASY 3xResNet12 (transductive)
84.29
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
ICI
84.01
Instance Credibility Inference for Few-Shot Learning
ASY ResNet12 (transductive)
83.98
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
TIM-GD
82.1
Transductive Information Maximization For Few-Shot Learning
BD-CSPN + ESFR (WRN)
81.77
Unsupervised Embedding Adaptation via Early-Stage Feature Reconstruction for Few-Shot Classification
LaplacianShot
80.30
Laplacian Regularized Few-Shot Learning
-
BD-CSPN + ESFR (ResNet-18)
80.13
Unsupervised Embedding Adaptation via Early-Stage Feature Reconstruction for Few-Shot Classification
HCTransformers
79.67
Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learning
EPNet
78.50
Embedding Propagation: Smoother Manifold for Few-Shot Classification
LST
77.7
Learning to Self-Train for Semi-Supervised Few-Shot Classification
OSLO
76.64
Open-Set Likelihood Maximization for Few-Shot Learning
pseudo-shots
76.55
Extended Few-Shot Learning: Exploiting Existing Resources for Novel Tasks
FewTURE
76.32
Rethinking Generalization in Few-Shot Classification
EASY 3xResNet12 (inductive)
74.71
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
MetaQDA
74.33
Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition
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