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 281–290 of 353 papers

Title	Date	Tasks	Status
Generative Adversarial Networks Based on Collaborative Learning and Attention Mechanism for Hyperspectral Image Classification	Apr 3, 2020	Few-Shot Image ClassificationGenerative Adversarial Network	—Unverified
PAC-Bayes meta-learning with implicit task-specific posteriors	Mar 5, 2020	Few-Shot Image ClassificationFew-Shot Learning	—Unverified
MaxUp: A Simple Way to Improve Generalization of Neural Network Training	Feb 20, 2020	Few-Shot Image ClassificationGeneral Classification	CodeCode Available
Few-Shot Learning as Domain Adaptation: Algorithm and Analysis	Feb 6, 2020	Domain AdaptationFew-Shot Image Classification	—Unverified
Asymmetric Distribution Measure for Few-shot Learning	Feb 1, 2020	Few-Shot Image ClassificationFew-Shot Learning	—Unverified
Graph-based Interpolation of Feature Vectors for Accurate Few-Shot Classification	Jan 27, 2020	Few-Shot Image ClassificationGeneral Classification	CodeCode Available
Optimized Generic Feature Learning for Few-shot Classification across Domains	Jan 22, 2020	BIG-bench Machine LearningClassification	—Unverified
Semantic Regularization: Improve Few-shot Image Classification by Reducing Meta Shift	Dec 18, 2019	DecoderFew-Shot Image Classification	—Unverified
Meta-Learning Initializations for Image Segmentation	Dec 13, 2019	Bayesian OptimizationFew-Shot Image Classification	CodeCode Available
Associative Alignment for Few-shot Image Classification	Dec 11, 2019	ClassificationDomain Adaptation	CodeCode Available

Show:10 25 50

← PrevPage 29 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