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Few Shot Image Classification On Mini 2

Métriques

Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

Paper TitleRepository
SgVA-CLIP97.95SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for Few-shot Image Classification
CAML [Laion-2b]96.2Context-Aware Meta-Learning
P>M>F (P=DINO-ViT-base, M=ProtoNet)95.3Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference
TRIDENT86.11Transductive Decoupled Variational Inference for Few-Shot Classification
PT+MAP+SF+SOT (transductive)85.59The Self-Optimal-Transport Feature Transform
PT+MAP+SF+BPA (transductive)85.59The Balanced-Pairwise-Affinities Feature Transform
PEMnE-BMS* (transductive)85.54Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning
PT+MAP (s+f) (transductive)84.81Few-Shot Learning by Integrating Spatial and Frequency Representation
BAVARDAGE84.80Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification-
EASY 3xResNet12 (transductive)84.04EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
Illumination Augmentation82.99Sill-Net: Feature Augmentation with Separated Illumination Representation
PT+MAP (transductive)82.92Leveraging the Feature Distribution in Transfer-based Few-Shot Learning
EASY 2xResNet12 1/√2 (transductive)82.31EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
Transductive CNAPS + FETI79.9Enhancing Few-Shot Image Classification with Unlabelled Examples
PrototypeCompletion79.01Prototype Completion for Few-Shot Learning
SemFew-Trans78.94Simple Semantic-Aided Few-Shot Learning
MCT78.55Meta-Learned Confidence for Few-shot Learning
TIM-GD77.80Transductive Information Maximization For Few-Shot Learning
Simple CNAPS + FETI77.4Improved Few-Shot Visual Classification
EPNet77.27Embedding Propagation: Smoother Manifold for Few-Shot Classification
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