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 171–180 of 353 papers

Title	Date	Tasks	Status	Score
Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning	May 25, 2018	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	5
Learning to Remember Rare Events	Mar 9, 2017	Few-Shot Image Classificationimage-classification	CodeCode Available	5
Logarithm-transform aided Gaussian Sampling for Few-Shot Learning	Sep 28, 2023	ClassificationFew-Shot Image Classification	CodeCode Available	5
Low-Shot Learning for the Semantic Segmentation of Remote Sensing Imagery	Mar 26, 2018	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	5
Low-shot learning with large-scale diffusion	Jun 7, 2017	Few-Shot Image Classificationgraph construction	CodeCode Available	5
Low-shot Visual Recognition by Shrinking and Hallucinating Features	Jun 9, 2016	BIG-bench Machine LearningFew-Shot Image Classification	CodeCode Available	5
MaxUp: A Simple Way to Improve Generalization of Neural Network Training	Feb 20, 2020	Few-Shot Image ClassificationGeneral Classification	CodeCode Available	5
MergedNET: A simple approach for one-shot learning in siamese networks based on similarity layers	Oct 14, 2022	Few-Shot Image ClassificationFine-Grained Image Classification	CodeCode Available	5
Meta-Curvature	Feb 9, 2019	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	5
MetaFun: Meta-Learning with Iterative Functional Updates	Dec 5, 2019	DecoderFew-Shot Image Classification	CodeCode Available	5

Show:10 25 50

← PrevPage 18 of 36Next →

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