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

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Showing 211–220 of 353 papers

Title	Date	Tasks	Status
MetaGAN: An Adversarial Approach to Few-Shot Learning	Dec 1, 2018	ClassificationFew-Shot Image Classification	—Unverified
Meta-OLE: Meta-learned Orthogonal Low-Rank Embedding	Sep 29, 2021	ClassificationFew-Shot Image Classification	—Unverified
Model-Agnostic Graph Regularization for Few-Shot Learning	Feb 14, 2021	Few-Shot Image ClassificationFew-Shot Learning	—Unverified
Model-Agnostic Meta-Learning for Multimodal Task Distributions	Sep 27, 2018	DiversityFew-Shot Image Classification	—Unverified
Modelling Multi-modal Cross-interaction for ML-FSIC Based on Local Feature Selection	Dec 18, 2024	feature selectionFew-Shot Image Classification	—Unverified
Multi-Domain Few-Shot Learning and Dataset for Agricultural Applications	Sep 21, 2021	Few-Shot Image ClassificationFew-Shot Learning	—Unverified
Multi-scale Adaptive Task Attention Network for Few-Shot Learning	Nov 30, 2020	Few-Shot Image ClassificationFew-Shot Learning	—Unverified
Multi-Similarity Contrastive Learning	Jul 6, 2023	Caption GenerationContrastive Learning	—Unverified
MVP: Meta Visual Prompt Tuning for Few-Shot Remote Sensing Image Scene Classification	Sep 17, 2023	Data AugmentationDiversity	—Unverified
NeurIPS'22 Cross-Domain MetaDL competition: Design and baseline results	Aug 31, 2022	Few-Shot Image ClassificationFew-Shot Learning	—Unverified

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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