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 276–300 of 353 papers

TitleStatusHype
DeepEMD: Few-Shot Image Classification With Differentiable Earth Mover's Distance and Structured Classifiers—0
Hyperspectral Image Classification of Convolutional Neural Network Combined with Valuable Samples—0
Boosting Few-Shot Learning With Adaptive Margin Loss—0
Compositional Few-Shot Recognition with Primitive Discovery and Enhancing—0
Divergent Search for Few-Shot Image Classification—0
Generative Adversarial Networks Based on Collaborative Learning and Attention Mechanism for Hyperspectral Image Classification—0
PAC-Bayes meta-learning with implicit task-specific posteriors—0
MaxUp: A Simple Way to Improve Generalization of Neural Network TrainingCode0
Few-Shot Learning as Domain Adaptation: Algorithm and Analysis—0
Asymmetric Distribution Measure for Few-shot Learning—0
Graph-based Interpolation of Feature Vectors for Accurate Few-Shot ClassificationCode0
Optimized Generic Feature Learning for Few-shot Classification across Domains—0
Semantic Regularization: Improve Few-shot Image Classification by Reducing Meta Shift—0
Meta-Learning Initializations for Image SegmentationCode0
Associative Alignment for Few-shot Image ClassificationCode0
Meta-Learning without MemorizationCode0
Improved Few-Shot Visual Classification—0
MetaFun: Meta-Learning with Iterative Functional UpdatesCode0
Prototype Rectification for Few-Shot Learning—0
Generalized Adaptation for Few-Shot Learning—0
Knowledge Graph Transfer Network for Few-Shot RecognitionCode0
Self-Supervised Learning For Few-Shot Image ClassificationCode0
Deep Metric Learning-Based Feature Embedding for Hyperspectral Image ClassificationCode0
Harnessing the Power of Infinitely Wide Deep Nets on Small-data TasksCode0
An empirical study of pretrained representations for few-shot 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+BPA (transductive)Accuracy85.59—Unverified
6PT+MAP+SF+SOT (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+SOT (transductive)Accuracy91.34—Unverified
9PT+MAP+SF+BPA (transductive)Accuracy91.34—Unverified
10AmdimNetAccuracy90.98—Unverified