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 91–100 of 353 papers

Title	Date	Tasks	Status	Hype
ProD: Prompting-To-Disentangle Domain Knowledge for Cross-Domain Few-Shot Image Classification	Jan 1, 2023	Cross-Domain Few-ShotDomain Generalization	—Unverified	0
Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image Classification	Nov 30, 2022	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Exploiting Category Names for Few-Shot Classification with Vision-Language Models	Nov 29, 2022	ClassificationFew-Shot Image Classification	—Unverified	0
SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for Few-shot Image Classification	Nov 28, 2022	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	0
Enhancing Few-shot Image Classification with Cosine Transformer	Nov 13, 2022	ClassificationFew-Shot Image Classification	CodeCode Available	1
KGTN-ens: Few-Shot Image Classification with Knowledge Graph Ensembles	Nov 6, 2022	Few-Shot Image Classificationimage-classification	CodeCode Available	0
Unsupervised Few-Shot Image Classification by Learning Features into Clustering Space	Oct 21, 2022	ClassificationClustering	CodeCode Available	1
Visual-Semantic Contrastive Alignment for Few-Shot Image Classification	Oct 20, 2022	ClassificationContrastive Learning	—Unverified	0
Few-Shot Learning of Compact Models via Task-Specific Meta Distillation	Oct 18, 2022	Few-Shot Image ClassificationFew-Shot Learning	—Unverified	0
MergedNET: A simple approach for one-shot learning in siamese networks based on similarity layers	Oct 14, 2022	Few-Shot Image ClassificationFine-Grained Image Classification	CodeCode Available	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