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 101125 of 353 papers

TitleStatusHype
Constellation Nets for Few-Shot LearningCode1
Few-Shot Learning via Embedding Adaptation with Set-to-Set FunctionsCode1
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few ExamplesCode1
Self-supervised Knowledge Distillation for Few-shot LearningCode1
Context-Aware Meta-LearningCode1
SemiReward: A General Reward Model for Semi-supervised LearningCode1
Model-Agnostic Meta-Learning for Fast Adaptation of Deep NetworksCode1
On First-Order Meta-Learning AlgorithmsCode1
Leveraging the Feature Distribution in Transfer-based Few-Shot LearningCode1
Leveraging Cross-Modal Neighbor Representation for Improved CLIP ClassificationCode1
Few-Shot Image Classification Benchmarks are Too Far From Reality: Build Back Better with Semantic Task SamplingCode1
Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple ClassifierCode1
Match Them Up: Visually Explainable Few-shot Image ClassificationCode1
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image ClassificationCode1
BECLR: Batch Enhanced Contrastive Few-Shot LearningCode1
Matching Networks for One Shot LearningCode1
Few-Shot Learning by Integrating Spatial and Frequency RepresentationCode1
Region Comparison Network for Interpretable Few-shot Image ClassificationCode1
Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image ClassificationCode1
Meta-Learned Confidence for Few-shot LearningCode1
Few-shot Relational Reasoning via Connection Subgraph PretrainingCode1
FewVS: A Vision-Semantics Integration Framework for Few-Shot Image ClassificationCode1
Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object InteractionsCode1
Few-Shot Classification with Feature Map Reconstruction NetworksCode1
Unsupervised Embedding Adaptation via Early-Stage Feature Reconstruction for Few-Shot ClassificationCode1
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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