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 126–150 of 353 papers

TitleStatusHype
iCAR: Bridging Image Classification and Image-text Alignment for Visual RecognitionCode0
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual ModelsCode4
Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a DifferenceCode1
A Simple Approach to Adversarial Robustness in Few-shot Image ClassificationCode0
Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot ClassificationCode1
The Self-Optimal-Transport Feature TransformCode1
Attribute Prototype Network for Any-Shot Learning—0
Matching Feature Sets for Few-Shot Image Classification—0
Multidimensional Belief Quantification for Label-Efficient Meta-LearningCode0
HyperShot: Few-Shot Learning by Kernel HyperNetworksCode1
Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot LearningCode1
Worst Case Matters for Few-Shot RecognitionCode1
Continuous-Time Meta-Learning with Forward Mode Differentiation—0
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple IngredientsCode1
Visual Representation Learning with Self-Supervised Attention for Low-Label High-data RegimeCode0
Multi-level Second-order Few-shot LearningCode0
HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot LearningCode1
Debiased Learning from Naturally Imbalanced Pseudo-LabelsCode1
Improving Adversarially Robust Few-Shot Image Classification With Generalizable Representations—0
Transformers Can Do Bayesian InferenceCode1
Exploring Category-correlated Feature for Few-shot Image Classification—0
Few-Shot Image Classification Along Sparse Graphs—0
Curriculum Meta-Learning for Few-shot ClassificationCode0
Inferring Prototypes for Multi-Label Few-Shot Image Classification with Word Vector Guided Attention—0
argmax centroid—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