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 1–25 of 353 papers

Title	Date	Tasks	Status	Hype
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models	Apr 19, 2022	FairnessFew-Shot Image Classification	CodeCode Available	4
The Balanced-Pairwise-Affinities Feature Transform	Jun 25, 2024	Few-Shot Image ClassificationImage Clustering	CodeCode Available	2
Prototypical Networks for Few-shot Learning	Mar 15, 2017	Category-Agnostic Pose EstimationFew-Shot Image Classification	CodeCode Available	2
GalLoP: Learning Global and Local Prompts for Vision-Language Models	Jul 1, 2024	DiversityDomain Generalization	CodeCode Available	2
Effective Data Augmentation With Diffusion Models	Feb 7, 2023	Data AugmentationDiversity	CodeCode Available	2
Learning Transferable Visual Models From Natural Language Supervision	Feb 26, 2021	Action RecognitionBenchmarking	CodeCode Available	2
AWT: Transferring Vision-Language Models via Augmentation, Weighting, and Transportation	Jul 5, 2024	Action RecognitionFew-Shot Image Classification	CodeCode Available	2
LibFewShot: A Comprehensive Library for Few-shot Learning	Sep 10, 2021	Data AugmentationFew-Shot Image Classification	CodeCode Available	2
Disentangled Feature Representation for Few-shot Image Classification	Sep 26, 2021	BenchmarkingClassification	CodeCode Available	1
Adaptive Subspaces for Few-Shot Learning	Jun 1, 2020	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions	May 27, 2022	BenchmarkingFew-Shot Image Classification	CodeCode Available	1
Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image Classification	Nov 30, 2022	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
Diffusion Mechanism in Residual Neural Network: Theory and Applications	May 7, 2021	Binary ClassificationClassification	CodeCode Available	1
Distilling Large Vision-Language Model with Out-of-Distribution Generalizability	Jul 6, 2023	Few-Shot Image ClassificationImage Classification	CodeCode Available	1
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification	Jun 20, 2022	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
BaseTransformers: Attention over base data-points for One Shot Learning	Oct 5, 2022	Few-Shot Image ClassificationOne-Shot Learning	CodeCode Available	1
Class-Aware Patch Embedding Adaptation for Few-Shot Image Classification	Jan 1, 2023	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Debiased Learning from Naturally Imbalanced Pseudo-Labels	Jan 5, 2022	counterfactualCounterfactual Reasoning	CodeCode Available	1
Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learning	Mar 17, 2022	AttributeFew-Shot Image Classification	CodeCode Available	1
Constellation Nets for Few-Shot Learning	Jan 1, 2021	ClusteringFew-Shot Image Classification	CodeCode Available	1
Context-Aware Meta-Learning	Oct 17, 2023	Few-Shot Image ClassificationIn-Context Learning	CodeCode Available	1
A Universal Representation Transformer Layer for Few-Shot Image Classification	Jun 21, 2020	ClassificationDomain Generalization	CodeCode Available	1
Automated Relational Meta-learning	Jan 3, 2020	Few-Shot Image Classificationimage-classification	CodeCode Available	1
Attentive Weights Generation for Few Shot Learning via Information Maximization	Jun 1, 2020	ClassificationFew-Shot Image Classification	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+BPA (transductive)	Accuracy	85.59	—	Unverified
6	PT+MAP+SF+SOT (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+SOT (transductive)	Accuracy	91.34	—	Unverified
9	PT+MAP+SF+BPA (transductive)	Accuracy	91.34	—	Unverified
10	AmdimNet	Accuracy	90.98	—	Unverified