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 121–130 of 353 papers

Title	Date	Tasks	Status	Hype
Task-Prior Conditional Variational Auto-Encoder for Few-Shot Image Classification	May 30, 2022	Few-Shot Image ClassificationFew-Shot Learning	—Unverified	0
Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions	May 27, 2022	BenchmarkingFew-Shot Image Classification	CodeCode Available	1
Uncertainty-based Network for Few-shot Image Classification	May 17, 2022	ClassificationFew-Shot Image Classification	—Unverified	0
Privacy Enhancement for Cloud-Based Few-Shot Learning	May 10, 2022	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	0
Few-Shot Image Classification Benchmarks are Too Far From Reality: Build Back Better with Semantic Task Sampling	May 10, 2022	Few-Shot Image ClassificationGeneral Classification	CodeCode Available	1
iCAR: Bridging Image Classification and Image-text Alignment for Visual Recognition	Apr 22, 2022	Action RecognitionClassification	CodeCode Available	0
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models	Apr 19, 2022	FairnessFew-Shot Image Classification	CodeCode Available	4
Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference	Apr 15, 2022	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
A Simple Approach to Adversarial Robustness in Few-shot Image Classification	Apr 11, 2022	Adversarial RobustnessFew-Shot Image Classification	CodeCode Available	0
Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification	Apr 9, 2022	Classificationdomain 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+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