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 311–320 of 353 papers

Title	Date	Tasks	Status
Small Sample Hyperspectral Image Classification Based on the Random Patches Network and Recursive Filtering	Feb 23, 2023	ClassificationDimensionality Reduction	CodeCode Available
Deep supervised learning for hyperspectral data classification through convolutional neural networks	Jul 26, 2015	ClassificationFew-Shot Image Classification	CodeCode Available
MetaFun: Meta-Learning with Iterative Functional Updates	Dec 5, 2019	DecoderFew-Shot Image Classification	CodeCode Available
Simultaneous Perturbation Method for Multi-Task Weight Optimization in One-Shot Meta-Learning	Oct 25, 2021	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available
Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network	Jan 13, 2017	ClassificationFew-Shot Image Classification	CodeCode Available
Meta-Curvature	Feb 9, 2019	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available
Neural TMDlayer: Modeling Instantaneous flow of features via SDE Generators	Aug 19, 2021	Data AugmentationFew-Shot Image Classification	CodeCode Available
ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation	Jul 12, 2025	Few-Shot Image Classificationimage-classification	CodeCode Available
Deep Metric Learning-Based Feature Embedding for Hyperspectral Image Classification	Oct 30, 2019	Domain AdaptationFew-Shot Image Classification	CodeCode Available
Adaptive Cross-Modal Few-Shot Learning	Feb 19, 2019	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available

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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