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 101–125 of 353 papers

Title	Date	Tasks	Status	Hype
Empirical Bayes Transductive Meta-Learning with Synthetic Gradients	Apr 27, 2020	Few-Shot Image ClassificationMeta-Learning	CodeCode Available	1
Negative Margin Matters: Understanding Margin in Few-shot Classification	Mar 26, 2020	ClassificationFew-Shot Image Classification	CodeCode Available	1
Instance Credibility Inference for Few-Shot Learning	Mar 26, 2020	Data AugmentationFew-Shot Image Classification	CodeCode Available	1
Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?	Mar 25, 2020	AllFew-Shot Image Classification	CodeCode Available	1
Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification	Mar 20, 2020	feature selectionFew-Shot Image Classification	CodeCode Available	1
DeepEMD: Differentiable Earth Mover's Distance for Few-Shot Learning	Mar 15, 2020	ClassificationFew-Shot Image Classification	CodeCode Available	1
Embedding Propagation: Smoother Manifold for Few-Shot Classification	Mar 9, 2020	ClassificationFew-Shot Image Classification	CodeCode Available	1
Meta-Learned Confidence for Few-shot Learning	Feb 27, 2020	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Automated Relational Meta-learning	Jan 3, 2020	Few-Shot Image Classificationimage-classification	CodeCode Available	1
A Broader Study of Cross-Domain Few-Shot Learning	Dec 16, 2019	Cross-Domain Few-Shotcross-domain few-shot learning	CodeCode Available	1
SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning	Nov 12, 2019	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation	Oct 30, 2019	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels	Oct 11, 2019	Bayesian InferenceDomain Adaptation	CodeCode Available	1
Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML	Sep 19, 2019	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Meta-Learning with Implicit Gradients	Sep 10, 2019	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Charting the Right Manifold: Manifold Mixup for Few-shot Learning	Jul 28, 2019	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes	Jun 18, 2019	Active LearningContinual Learning	CodeCode Available	1
Meta-Learning with Differentiable Convex Optimization	Apr 7, 2019	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples	Mar 7, 2019	Few-Shot Image ClassificationGeneral Classification	CodeCode Available	1
Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions	Dec 10, 2018	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Meta-Transfer Learning for Few-Shot Learning	Dec 6, 2018	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
How to train your MAML	Oct 22, 2018	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks	Oct 1, 2018	3D Shape RecognitionDecoder	CodeCode Available	1
Dynamic Few-Shot Visual Learning without Forgetting	Apr 25, 2018	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
On First-Order Meta-Learning Algorithms	Mar 8, 2018	Few-Shot Image ClassificationFew-Shot Learning	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