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 81–90 of 353 papers

Title	Date	Tasks	Status	Hype	Score
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients	Jan 24, 2022	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1	5
Leveraging Cross-Modal Neighbor Representation for Improved CLIP Classification	Apr 27, 2024	Contrastive LearningFew-Shot Image Classification	CodeCode Available	1	5
Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes	Jun 18, 2019	Active LearningContinual Learning	CodeCode Available	1	5
Improving ProtoNet for Few-Shot Video Object Recognition: Winner of ORBIT Challenge 2022	Oct 1, 2022	Few-Shot Image ClassificationObject Recognition	CodeCode Available	1	5
Embedding Propagation: Smoother Manifold for Few-Shot Classification	Mar 9, 2020	ClassificationFew-Shot Image Classification	CodeCode Available	1	5
Empirical Bayes Transductive Meta-Learning with Synthetic Gradients	Apr 27, 2020	Few-Shot Image ClassificationMeta-Learning	CodeCode Available	1	5
Enhancing Few-Shot Image Classification through Learnable Multi-Scale Embedding and Attention Mechanisms	Sep 12, 2024	Cross-Domain Few-ShotFew-Shot Image Classification	CodeCode Available	1	5
Enhancing Few-shot Image Classification with Cosine Transformer	Nov 13, 2022	ClassificationFew-Shot Image Classification	CodeCode Available	1	5
Cross-domain Few-shot Learning with Task-specific Adapters	Jul 1, 2021	Cross-Domain Few-Shotcross-domain few-shot learning	CodeCode Available	1	5
Laplacian Regularized Few-Shot Learning	Jun 28, 2020	ClusteringFew-Shot Image Classification	CodeCode Available	1	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+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