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 176200 of 353 papers

TitleStatusHype
Rectifying the Shortcut Learning of Background for Few-Shot LearningCode1
Memory Efficient Meta-Learning with Large ImagesCode1
Cross-domain Few-shot Learning with Task-specific AdaptersCode1
SITTA: Single Image Texture Translation for Data AugmentationCode1
Unsupervised Embedding Adaptation via Early-Stage Feature Reconstruction for Few-Shot ClassificationCode1
Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective AdaptationCode1
Contextualizing Meta-Learning via Learning to DecomposeCode0
Scaling Vision with Sparse Mixture of ExpertsCode1
Scaling Vision TransformersCode0
Deep Metric Learning for Few-Shot Image Classification: A Review of Recent Developments0
Few-Shot Learning by Integrating Spatial and Frequency RepresentationCode1
MetaKernel: Learning Variational Random Features with Limited LabelsCode0
Diffusion Mechanism in Residual Neural Network: Theory and ApplicationsCode1
Few-Shot Learning for Image Classification of Common FloraCode0
Local descriptor-based multi-prototype network for few-shot Learning0
Subspace Representation Learning for Few-shot Image Classification0
Rich Semantics Improve Few-shot Learning0
PanGu-α: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel ComputationCode1
Prototypical Region Proposal Networks for Few-Shot Localization and Classification0
Few-Shot Action Recognition with Compromised Metric via Optimal Transport0
ORBIT: A Real-World Few-Shot Dataset for Teachable Object RecognitionCode1
Universal Representation Learning from Multiple Domains for Few-shot ClassificationCode1
Multi-level Metric Learning for Few-shot Image RecognitionCode0
Hierarchical Representation based Query-Specific Prototypical Network for Few-Shot Image Classification0
How to distribute data across tasks for meta-learning?0
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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+SOT (transductive)Accuracy85.59Unverified
6PT+MAP+SF+BPA (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+BPA (transductive)Accuracy91.34Unverified
9PT+MAP+SF+SOT (transductive)Accuracy91.34Unverified
10AmdimNetAccuracy90.98Unverified