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

Showing 301325 of 353 papers

TitleStatusHype
Diversity with Cooperation: Ensemble Methods for Few-Shot ClassificationCode0
Meta-learning algorithms for Few-Shot Computer VisionCode0
Simple Semi-supervised Knowledge Distillation from Vision-Language Models via Dual-Head OptimizationCode0
DiffKendall: A Novel Approach for Few-Shot Learning with Differentiable Kendall's Rank CorrelationCode0
Multidimensional Belief Quantification for Label-Efficient Meta-LearningCode0
Delta-encoder: an effective sample synthesis method for few-shot object recognitionCode0
Multi-Level Correlation Network For Few-Shot Image ClassificationCode0
Multi-level Metric Learning for Few-shot Image RecognitionCode0
Multi-level Second-order Few-shot LearningCode0
MetaKernel: Learning Variational Random Features with Limited LabelsCode0
Small Sample Hyperspectral Image Classification Based on the Random Patches Network and Recursive FilteringCode0
Deep supervised learning for hyperspectral data classification through convolutional neural networksCode0
MetaFun: Meta-Learning with Iterative Functional UpdatesCode0
Simultaneous Perturbation Method for Multi-Task Weight Optimization in One-Shot Meta-LearningCode0
Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural NetworkCode0
Meta-CurvatureCode0
Neural TMDlayer: Modeling Instantaneous flow of features via SDE GeneratorsCode0
ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark EvaluationCode0
Deep Metric Learning-Based Feature Embedding for Hyperspectral Image ClassificationCode0
Adaptive Cross-Modal Few-Shot LearningCode0
MergedNET: A simple approach for one-shot learning in siamese networks based on similarity layersCode0
MaxUp: A Simple Way to Improve Generalization of Neural Network TrainingCode0
On the Efficacy of Differentially Private Few-shot Image ClassificationCode0
Low-shot Visual Recognition by Shrinking and Hallucinating FeaturesCode0
Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-LearningCode0
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Benchmark Results

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