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 201225 of 353 papers

TitleStatusHype
Sign-MAML: Efficient Model-Agnostic Meta-Learning by SignSGDCode0
Simple Semi-supervised Knowledge Distillation from Vision-Language Models via Dual-Head OptimizationCode0
Small Sample Hyperspectral Image Classification Based on the Random Patches Network and Recursive FilteringCode0
Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural NetworkCode0
Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-LearningCode0
Subspace Adaptation Prior for Few-Shot LearningCode0
TADAM: Task dependent adaptive metric for improved few-shot learningCode0
TextCaps : Handwritten Character Recognition with Very Small DatasetsCode0
Tiny models from tiny data: Textual and null-text inversion for few-shot distillationCode0
Towards a Neural StatisticianCode0
Unsupervised Image Classification for Deep Representation LearningCode0
Visual Representation Learning with Self-Supervised Attention for Low-Label High-data RegimeCode0
ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark EvaluationCode0
Meta-OLE: Meta-learned Orthogonal Low-Rank Embedding0
GCCN: Global Context Convolutional Network0
Model-Agnostic Graph Regularization for Few-Shot Learning0
Frozen Feature Augmentation for Few-Shot Image Classification0
Model-Agnostic Meta-Learning for Multimodal Task Distributions0
Modelling Multi-modal Cross-interaction for ML-FSIC Based on Local Feature Selection0
Baby steps towards few-shot learning with multiple semantics0
Multi-Domain Few-Shot Learning and Dataset for Agricultural Applications0
A Unified Framework with Meta-dropout for Few-shot Learning0
Augmented Conditioning Is Enough For Effective Training Image Generation0
Attribute Prototype Network for Any-Shot Learning0
FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?0
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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