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 71–80 of 353 papers

Title	Date	Tasks	Status	Hype
Multistage Relation Network With Dual-Metric for Few-Shot Hyperspectral Image Classification	Apr 28, 2023	ClassificationFew-Shot Image Classification	CodeCode Available	1
ESPT: A Self-Supervised Episodic Spatial Pretext Task for Improving Few-Shot Learning	Apr 26, 2023	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue Distribution	Apr 4, 2023	DisentanglementDomain Generalization	CodeCode Available	1
Strong Baselines for Parameter Efficient Few-Shot Fine-tuning	Apr 4, 2023	Few-Shot Image Classificationimage-classification	—Unverified	0
Meta-Learning with a Geometry-Adaptive Preconditioner	Apr 4, 2023	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Boosting Few-Shot Text Classification via Distribution Estimation	Mar 26, 2023	ClassificationFew-Shot Image Classification	—Unverified	0
Prompt Tuning based Adapter for Vision-Language Model Adaption	Mar 24, 2023	Few-Shot Image Classificationimage-classification	CodeCode Available	1
The effectiveness of MAE pre-pretraining for billion-scale pretraining	Mar 23, 2023	Action ClassificationAction Recognition	CodeCode Available	1
RotoGBML: Towards Out-of-Distribution Generalization for Gradient-Based Meta-Learning	Mar 12, 2023	Few-Shot Image Classificationimage-classification	—Unverified	0
Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning	Mar 10, 2023	Few-Shot Image Classificationimage-classification	—Unverified	0

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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