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 1–10 of 353 papers

TitleStatusHype
ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark EvaluationCode0
Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts—0
Provably Improving Generalization of Few-Shot Models with Synthetic Data—0
Simple Semi-supervised Knowledge Distillation from Vision-Language Models via Dual-Head OptimizationCode0
Brain Inspired Adaptive Memory Dual-Net for Few-Shot Image Classification—0
InPK: Infusing Prior Knowledge into Prompt for Vision-Language Models—0
Augmented Conditioning Is Enough For Effective Training Image Generation—0
Geometric Mean Improves Loss For Few-Shot Learning—0
A Cognitive Paradigm Approach to Probe the Perception-Reasoning Interface in VLMs—0
IDEA: Image Description Enhanced CLIP-AdapterCode0
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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
1CAML [Laion-2b]Accuracy95.8—Unverified
2PT+MAP+SF+BPA (transductive)Accuracy95.8—Unverified
3PT+MAP+SF+SOT (transductive)Accuracy95.8—Unverified
4PT+MAP+SF (transductive)Accuracy95.48—Unverified
5PEMnE-BMS*Accuracy94.78—Unverified
6Illumination AugmentationAccuracy94.73—Unverified
7LST+MAPAccuracy91.68—Unverified
8PT+MAPAccuracy91.55—Unverified
9EASY 3xResNet12 (transductive)Accuracy90.56—Unverified
10EASY 4xResNet12 (transductive)Accuracy90.5—Unverified