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

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Showing 201–250 of 353 papers

TitleStatusHype
Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot LearningCode1
Learning Transferable Visual Models From Natural Language SupervisionCode2
MetaDelta: A Meta-Learning System for Few-shot Image ClassificationCode1
Model-Agnostic Graph Regularization for Few-Shot Learning—0
Transfer learning based few-shot classification using optimal transport mapping from preprocessed latent space of backbone neural networkCode1
Sill-Net: Feature Augmentation with Separated Illumination RepresentationCode1
Few-shot Image Classification with Multi-Facet Prototypes—0
CORL: Compositional Representation Learning for Few-Shot Classification—0
Machine learning with limited data—0
Shallow Bayesian Meta Learning for Real-World Few-Shot RecognitionCode1
Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple ClassifierCode1
Optimal allocation of data across training tasks in meta-learning—0
Constellation Nets for Few-Shot LearningCode1
Extended Few-Shot Learning: Exploiting Existing Resources for Novel TasksCode1
Few-Shot Classification with Feature Map Reconstruction NetworksCode1
Multi-scale Adaptive Task Attention Network for Few-Shot Learning—0
Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot TasksCode0
Match Them Up: Visually Explainable Few-shot Image ClassificationCode1
Mixture-based Feature Space Learning for Few-shot Image ClassificationCode1
Confusable Learning for Large-class Few-Shot Classification—0
How Does the Task Landscape Affect MAML Performance?—0
Uncertainty-Aware Few-Shot Image Classification—0
Variational Feature Disentangling for Fine-Grained Few-Shot Classification—0
Robust High-dimensional Memory-augmented Neural Networks—0
Improving Few-Shot Visual Classification with Unlabelled Examples—0
Region Comparison Network for Interpretable Few-shot Image ClassificationCode1
GPU-based Self-Organizing Maps for Post-Labeled Few-Shot Unsupervised LearningCode1
Yet Meta Learning Can Adapt Fast, It Can Also Break Easily—0
Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction—0
Transductive Information Maximization For Few-Shot LearningCode1
Few-Shot Image Classification via Contrastive Self-Supervised Learning—0
Complementing Representation Deficiency in Few-shot Image Classification: A Meta-Learning ApproachCode0
Impact of base dataset design on few-shot image classification—0
Layer-Wise Adaptive Updating for Few-Shot Image Classification—0
Generalized Few-Shot Video Classification with Video Retrieval and Feature GenerationCode1
Laplacian Regularized Few-Shot LearningCode1
Laplacian Regularized Few-Shot LearningCode1
A Universal Representation Transformer Layer for Few-Shot Image ClassificationCode1
Gradient-EM Bayesian Meta-learning—0
Unsupervised Image Classification for Deep Representation LearningCode0
Enhancing Few-Shot Image Classification with Unlabelled Examples—0
Self-supervised Knowledge Distillation for Few-shot LearningCode1
Leveraging the Feature Distribution in Transfer-based Few-Shot LearningCode1
Hyperspectral Image Classification of Convolutional Neural Network Combined with Valuable Samples—0
DeepEMD: Few-Shot Image Classification With Differentiable Earth Mover's Distance and Structured Classifiers—0
Adaptive Subspaces for Few-Shot LearningCode1
Attentive Weights Generation for Few Shot Learning via Information MaximizationCode1
ePillID Dataset: A Low-Shot Fine-Grained Benchmark for Pill IdentificationCode1
Boosting Few-Shot Learning With Adaptive Margin Loss—0
Compositional Few-Shot Recognition with Primitive Discovery and Enhancing—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