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
Simultaneous Perturbation Method for Multi-Task Weight Optimization in One-Shot Meta-LearningCode0
GCCN: Global Context Convolutional Network—0
A Closer Look at Prototype Classifier for Few-shot Image Classification—0
Self-Supervised Prime-Dual Networks for Few-Shot Image Classification—0
Meta-OLE: Meta-learned Orthogonal Low-Rank Embedding—0
MDFL: A UNIFIED FRAMEWORK WITH META-DROPOUT FOR FEW-SHOT LEARNING—0
Dataset Bias Prediction for Few-Shot Image Classification—0
Clustered Task-Aware Meta-Learning by Learning from Learning PathsCode0
Assessing two novel distance-based loss functions for few-shot image classification—0
ST-MAML: A Stochastic-Task based Method for Task-Heterogeneous Meta-Learning—0
Multi-Domain Few-Shot Learning and Dataset for Agricultural Applications—0
Ontology-based n-ball Concept Embeddings Informing Few-shot Image Classification—0
Sign-MAML: Efficient Model-Agnostic Meta-Learning by SignSGDCode0
Partner-Assisted Learning for Few-Shot Image Classification—0
Neural TMDlayer: Modeling Instantaneous flow of features via SDE GeneratorsCode0
Contextualizing Meta-Learning via Learning to DecomposeCode0
Scaling Vision Transformers—0
Deep Metric Learning for Few-Shot Image Classification: A Review of Recent Developments—0
MetaKernel: Learning Variational Random Features with Limited LabelsCode0
Few-Shot Learning for Image Classification of Common FloraCode0
Local descriptor-based multi-prototype network for few-shot Learning—0
Subspace Representation Learning for Few-shot Image Classification—0
Rich Semantics Improve Few-shot Learning—0
Few-Shot Action Recognition with Compromised Metric via Optimal Transport—0
Prototypical Region Proposal Networks for Few-Shot Localization and Classification—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