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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 211–220 of 353 papers

Title	Date	Tasks	Status	Score
Unsupervised Image Classification for Deep Representation Learning	Jun 20, 2020	ClassificationClustering	CodeCode Available	5
Visual Representation Learning with Self-Supervised Attention for Low-Label High-data Regime	Jan 22, 2022	Few-Shot Image Classificationimage-classification	CodeCode Available	5
ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation	Jul 12, 2025	Few-Shot Image Classificationimage-classification	CodeCode Available	5
Meta-OLE: Meta-learned Orthogonal Low-Rank Embedding	Sep 29, 2021	ClassificationFew-Shot Image Classification	—Unverified	0
GCCN: Global Context Convolutional Network	Oct 22, 2021	Few-Shot Image ClassificationFew-Shot Learning	—Unverified	0
Model-Agnostic Graph Regularization for Few-Shot Learning	Feb 14, 2021	Few-Shot Image ClassificationFew-Shot Learning	—Unverified	0
Frozen Feature Augmentation for Few-Shot Image Classification	Mar 15, 2024	ClassificationData Augmentation	—Unverified	0
Model-Agnostic Meta-Learning for Multimodal Task Distributions	Sep 27, 2018	DiversityFew-Shot Image Classification	—Unverified	0
Modelling Multi-modal Cross-interaction for ML-FSIC Based on Local Feature Selection	Dec 18, 2024	feature selectionFew-Shot Image Classification	—Unverified	0
Baby steps towards few-shot learning with multiple semantics	Jun 5, 2019	Few-Shot Image ClassificationFew-Shot Learning	—Unverified	0

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All datasets Mini-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-shot CUB 200 5-way 5-shot FC100 5-way (1-shot)FC100 5-way (5-shot)Meta-Dataset OMNIGLOT - 1-Shot, 20-way

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	SgVA-CLIP	Accuracy	97.95	—	Unverified
2	CAML [Laion-2b]	Accuracy	96.2	—	Unverified
3	P>M>F (P=DINO-ViT-base, M=ProtoNet)	Accuracy	95.3	—	Unverified
4	TRIDENT	Accuracy	86.11	—	Unverified
5	PT+MAP+SF+SOT (transductive)	Accuracy	85.59	—	Unverified
6	PT+MAP+SF+BPA (transductive)	Accuracy	85.59	—	Unverified
7	PEMnE-BMS* (transductive)	Accuracy	85.54	—	Unverified
8	PT+MAP (s+f) (transductive)	Accuracy	84.81	—	Unverified
9	BAVARDAGE	Accuracy	84.8	—	Unverified
10	EASY 3xResNet12 (transductive)	Accuracy	84.04	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SgVA-CLIP	Accuracy	98.72	—	Unverified
2	CAML [Laion-2b]	Accuracy	98.6	—	Unverified
3	P>M>F (P=DINO-ViT-base, M=ProtoNet)	Accuracy	98.4	—	Unverified
4	TRIDENT	Accuracy	95.95	—	Unverified
5	BAVARDAGE	Accuracy	91.65	—	Unverified
6	PEMnE-BMS*(transductive)	Accuracy	91.53	—	Unverified
7	Transductive CNAPS + FETI	Accuracy	91.5	—	Unverified
8	PT+MAP+SF+BPA (transductive)	Accuracy	91.34	—	Unverified
9	PT+MAP+SF+SOT (transductive)	Accuracy	91.34	—	Unverified
10	AmdimNet	Accuracy	90.98	—	Unverified