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
1BAVARDAGEAccuracy70.6—Unverified
2R2-D2+Task AugAccuracy67.66—Unverified
3MetaOptNet-SVM+Task AugAccuracy67.17—Unverified
4ACC + AmphibianAccuracy66.9—Unverified
5EASY 3xResNet12 (transductive)Accuracy66.86—Unverified
6HCTransformersAccuracy66.42—Unverified
7MSENetAccuracy66.27—Unverified
8EASY 2xResNet12 1/√2 (transductive)Accuracy65.82—Unverified
9Invariance-EquivarianceAccuracy65.3—Unverified
10EASY 3xResNet12 (inductive)Accuracy64.74—Unverified