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 126–150 of 353 papers

Title	Date	Tasks	Status	Hype	Score
Transformers Can Do Bayesian Inference	Dec 20, 2021	AutoMLBayesian Inference	CodeCode Available	1	5
Instance Credibility Inference for Few-Shot Learning	Mar 26, 2020	Data AugmentationFew-Shot Image Classification	CodeCode Available	1	5
Generalized Few-Shot Video Classification with Video Retrieval and Feature Generation	Jul 9, 2020	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1	5
On the Efficacy of Differentially Private Few-shot Image Classification	Feb 2, 2023	Federated LearningFew-Shot Image Classification	CodeCode Available	0	5
Assessing Sample Quality via the Latent Space of Generative Models	Jul 21, 2024	Few-Shot Image Classificationimage-classification	CodeCode Available	0	5
Data-Efficient Language Shaped Few-shot Image Classification	Nov 1, 2021	ClassificationFew-Shot Image Classification	CodeCode Available	0	5
Few-Shot Learning with Global Class Representations	Aug 14, 2019	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	0	5
Curriculum Meta-Learning for Few-shot Classification	Dec 6, 2021	ClassificationFew-Shot Image Classification	CodeCode Available	0	5
Few-Shot Learning for Image Classification of Common Flora	May 7, 2021	ClassificationFew-Shot Image Classification	CodeCode Available	0	5
Support-Set Context Matters for Bongard Problems	Sep 7, 2023	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	0	5
Few-Shot Image Recognition by Predicting Parameters from Activations	Jun 12, 2017	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	0	5
Neural TMDlayer: Modeling Instantaneous flow of features via SDE Generators	Aug 19, 2021	Data AugmentationFew-Shot Image Classification	CodeCode Available	0	5
Image Deformation Meta-Networks for One-Shot Learning	May 28, 2019	Few-Shot Image ClassificationMeta-Learning	CodeCode Available	0	5
Cooperative Meta-Learning with Gradient Augmentation	Jun 7, 2024	Few-Shot Image Classificationimage-classification	CodeCode Available	0	5
Simultaneous Perturbation Method for Multi-Task Weight Optimization in One-Shot Meta-Learning	Oct 25, 2021	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	0	5
Multidimensional Belief Quantification for Label-Efficient Meta-Learning	Mar 23, 2022	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	0	5
A Simple Neural Attentive Meta-Learner	Jul 11, 2017	Few-Shot Image ClassificationMeta-Learning	CodeCode Available	0	5
Multi-Level Correlation Network For Few-Shot Image Classification	Dec 4, 2024	Few-Shot Image Classificationimage-classification	CodeCode Available	0	5
A Simple Approach to Adversarial Robustness in Few-shot Image Classification	Apr 11, 2022	Adversarial RobustnessFew-Shot Image Classification	CodeCode Available	0	5
Multi-level Metric Learning for Few-shot Image Recognition	Mar 21, 2021	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	0	5
Few and Fewer: Learning Better from Few Examples Using Fewer Base Classes	Jan 29, 2024	Cross-Domain Few-ShotFew-Shot Image Classification	CodeCode Available	0	5
Meta-Learning without Memorization	Dec 9, 2019	Few-Shot Image Classificationimage-classification	CodeCode Available	0	5
Contextualizing Meta-Learning via Learning to Decompose	Jun 15, 2021	AttributeFew-Shot Image Classification	CodeCode Available	0	5
Multi-level Second-order Few-shot Learning	Jan 15, 2022	Action RecognitionFew-Shot action recognition	CodeCode Available	0	5
Meta-Learning Initializations for Image Segmentation	Dec 13, 2019	Bayesian OptimizationFew-Shot Image Classification	CodeCode Available	0	5

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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