SOTAVerified

Few-Shot Image Classification

Few-Shot Image Classification is a computer vision task that involves training machine learning models to classify images into predefined categories using only a few labeled examples of each category (typically ( Image credit: Learning Embedding Adaptation for Few-Shot Learning )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 226–250 of 353 papers

TitleStatusHype
PAC-Bayes meta-learning with implicit task-specific posteriors—0
PaLI: A Jointly-Scaled Multilingual Language-Image Model—0
Partner-Assisted Learning for Few-Shot Image Classification—0
p-Meta: Towards On-device Deep Model Adaptation—0
Probabilistic Model-Agnostic Meta-Learning—0
ProD: Prompting-To-Disentangle Domain Knowledge for Cross-Domain Few-Shot Image Classification—0
PrototypeFormer: Learning to Explore Prototype Relationships for Few-shot Image Classification—0
Prototype Rectification for Few-Shot Learning—0
Prototypical Region Proposal Networks for Few-Shot Localization and Classification—0
Provably Improving Generalization of Few-Shot Models with Synthetic Data—0
Rapid Adaptation with Conditionally Shifted Neurons—0
Revisiting Metric Learning for Few-Shot Image Classification—0
Rich Semantics Improve Few-shot Learning—0
Robust High-dimensional Memory-augmented Neural Networks—0
RotoGBML: Towards Out-of-Distribution Generalization for Gradient-Based Meta-Learning—0
Scaling Vision Transformers—0
Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction—0
Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification—0
Self-Supervised Prime-Dual Networks for Few-Shot Image Classification—0
Semantic Token Reweighting for Interpretable and Controllable Text Embeddings in CLIP—0
Semi-Supervised Risk Control via Prediction-Powered Inference—0
Siamese Transformer Networks for Few-shot Image Classification—0
Spot and Learn: A Maximum-Entropy Patch Sampler for Few-Shot Image Classification—0
ST-MAML: A Stochastic-Task based Method for Task-Heterogeneous Meta-Learning—0
Strong Baselines for Parameter Efficient Few-Shot Fine-tuning—0
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All datasetsMini-Imagenet 5-way (1-shot)Mini-Imagenet 5-way (5-shot)Tiered ImageNet 5-way (5-shot)Tiered ImageNet 5-way (1-shot)CIFAR-FS 5-way (5-shot)CIFAR-FS 5-way (1-shot)CUB 200 5-way 1-shotCUB 200 5-way 5-shotFC100 5-way (1-shot)FC100 5-way (5-shot)Meta-DatasetOMNIGLOT - 1-Shot, 20-way

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SgVA-CLIPAccuracy97.95—Unverified
2CAML [Laion-2b]Accuracy96.2—Unverified
3P>M>F (P=DINO-ViT-base, M=ProtoNet)Accuracy95.3—Unverified
4TRIDENTAccuracy86.11—Unverified
5PT+MAP+SF+SOT (transductive)Accuracy85.59—Unverified
6PT+MAP+SF+BPA (transductive)Accuracy85.59—Unverified
7PEMnE-BMS* (transductive)Accuracy85.54—Unverified
8PT+MAP (s+f) (transductive)Accuracy84.81—Unverified
9BAVARDAGEAccuracy84.8—Unverified
10EASY 3xResNet12 (transductive)Accuracy84.04—Unverified
#ModelMetricClaimedVerifiedStatus
1SgVA-CLIPAccuracy98.72—Unverified
2CAML [Laion-2b]Accuracy98.6—Unverified
3P>M>F (P=DINO-ViT-base, M=ProtoNet)Accuracy98.4—Unverified
4TRIDENTAccuracy95.95—Unverified
5BAVARDAGEAccuracy91.65—Unverified
6PEMnE-BMS*(transductive)Accuracy91.53—Unverified
7Transductive CNAPS + FETIAccuracy91.5—Unverified
8PT+MAP+SF+BPA (transductive)Accuracy91.34—Unverified
9PT+MAP+SF+SOT (transductive)Accuracy91.34—Unverified
10AmdimNetAccuracy90.98—Unverified