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
1GCRAccuracy99.63—Unverified
2DCN6-EAccuracy99.11—Unverified
3DCN4Accuracy98.8—Unverified
4TapNetAccuracy98.07—Unverified
5MAML++Accuracy97.7—Unverified
6MAML++Accuracy97.65—Unverified
7Relation NetAccuracy97.6—Unverified
8APLAccuracy97.2—Unverified
9MT-netAccuracy96.2—Unverified
10iMAML, Hessian-FreeAccuracy96.18—Unverified