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 76100 of 353 papers

TitleStatusHype
ORBIT: A Real-World Few-Shot Dataset for Teachable Object RecognitionCode1
Universal Representation Learning from Multiple Domains for Few-shot ClassificationCode1
Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot LearningCode1
MetaDelta: A Meta-Learning System for Few-shot Image ClassificationCode1
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
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
Constellation Nets for Few-Shot LearningCode1
Extended Few-Shot Learning: Exploiting Existing Resources for Novel TasksCode1
Few-Shot Classification with Feature Map Reconstruction NetworksCode1
Match Them Up: Visually Explainable Few-shot Image ClassificationCode1
Mixture-based Feature Space Learning for Few-shot Image ClassificationCode1
Region Comparison Network for Interpretable Few-shot Image ClassificationCode1
GPU-based Self-Organizing Maps for Post-Labeled Few-Shot Unsupervised LearningCode1
Transductive Information Maximization For Few-Shot LearningCode1
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
Self-supervised Knowledge Distillation for Few-shot LearningCode1
Leveraging the Feature Distribution in Transfer-based Few-Shot LearningCode1
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
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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+BPA (transductive)Accuracy85.59Unverified
6PT+MAP+SF+SOT (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+SOT (transductive)Accuracy91.34Unverified
9PT+MAP+SF+BPA (transductive)Accuracy91.34Unverified
10AmdimNetAccuracy90.98Unverified