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 301–310 of 353 papers

Title	Date	Tasks	Status	Hype
Learning to learn via Self-Critique	May 24, 2019	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	0
Hierarchically Structured Meta-learning	May 13, 2019	ClusteringContinual Learning	CodeCode Available	0
Edge-labeling Graph Neural Network for Few-shot Learning	May 4, 2019	ClusteringFew-Shot Image Classification	CodeCode Available	0
Amortized Bayesian Meta-Learning	May 1, 2019	Few-Shot Image ClassificationFew-Shot Learning	—Unverified	0
TextCaps : Handwritten Character Recognition with Very Small Datasets	Apr 17, 2019	Few-Shot Image ClassificationImage Classification	CodeCode Available	0
Meta-Learning with Differentiable Convex Optimization	Apr 7, 2019	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning	Mar 28, 2019	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	0
Diversity with Cooperation: Ensemble Methods for Few-Shot Classification	Mar 27, 2019	ClassificationDiversity	CodeCode Available	0
Enhancing Generalization of First-Order Meta-Learning	Mar 22, 2019	Few-Shot Image Classificationimage-classification	—Unverified	0
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples	Mar 7, 2019	Few-Shot Image ClassificationGeneral Classification	CodeCode Available	1

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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