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 226–250 of 353 papers

Title	Date	Tasks	Status	Hype
Region Comparison Network for Interpretable Few-shot Image Classification	Sep 8, 2020	ClassificationFew-Shot Image Classification	CodeCode Available	1
GPU-based Self-Organizing Maps for Post-Labeled Few-Shot Unsupervised Learning	Sep 4, 2020	ClassificationClustering	CodeCode Available	1
Yet Meta Learning Can Adapt Fast, It Can Also Break Easily	Sep 2, 2020	Few-Shot Image Classificationimage-classification	—Unverified	0
Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction	Sep 2, 2020	feature selectionFew-Shot Image Classification	—Unverified	0
Transductive Information Maximization For Few-Shot Learning	Aug 25, 2020	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Few-Shot Image Classification via Contrastive Self-Supervised Learning	Aug 23, 2020	ClassificationFew-Shot Image Classification	—Unverified	0
Complementing Representation Deficiency in Few-shot Image Classification: A Meta-Learning Approach	Jul 21, 2020	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	0
Impact of base dataset design on few-shot image classification	Jul 17, 2020	ClassificationFew-Shot Image Classification	—Unverified	0
Layer-Wise Adaptive Updating for Few-Shot Image Classification	Jul 16, 2020	ClassificationFew-Shot Image Classification	—Unverified	0
Generalized Few-Shot Video Classification with Video Retrieval and Feature Generation	Jul 9, 2020	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Laplacian Regularized Few-Shot Learning	Jun 29, 2020	ClusteringFew-Shot Image Classification	CodeCode Available	1
Laplacian Regularized Few-Shot Learning	Jun 28, 2020	ClusteringFew-Shot Image Classification	CodeCode Available	1
A Universal Representation Transformer Layer for Few-Shot Image Classification	Jun 21, 2020	ClassificationDomain Generalization	CodeCode Available	1
Gradient-EM Bayesian Meta-learning	Jun 21, 2020	Computational EfficiencyFew-Shot Image Classification	—Unverified	0
Unsupervised Image Classification for Deep Representation Learning	Jun 20, 2020	ClassificationClustering	CodeCode Available	0
Enhancing Few-Shot Image Classification with Unlabelled Examples	Jun 17, 2020	ClassificationClustering	—Unverified	0
Self-supervised Knowledge Distillation for Few-shot Learning	Jun 17, 2020	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Leveraging the Feature Distribution in Transfer-based Few-Shot Learning	Jun 6, 2020	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Hyperspectral Image Classification of Convolutional Neural Network Combined with Valuable Samples	Jun 1, 2020	Active LearningClassification	—Unverified	0
DeepEMD: Few-Shot Image Classification With Differentiable Earth Mover's Distance and Structured Classifiers	Jun 1, 2020	ClassificationFew-Shot Image Classification	—Unverified	0
Adaptive Subspaces for Few-Shot Learning	Jun 1, 2020	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Attentive Weights Generation for Few Shot Learning via Information Maximization	Jun 1, 2020	ClassificationFew-Shot Image Classification	CodeCode Available	1
ePillID Dataset: A Low-Shot Fine-Grained Benchmark for Pill Identification	May 28, 2020	Few-Shot Image ClassificationFine-Grained Image Recognition	CodeCode Available	1
Boosting Few-Shot Learning With Adaptive Margin Loss	May 28, 2020	Few-Shot Image ClassificationFew-Shot Learning	—Unverified	0
Compositional Few-Shot Recognition with Primitive Discovery and Enhancing	May 12, 2020	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