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 326–350 of 353 papers

Title	Date	Tasks	Status
Low-shot learning with large-scale diffusion	Jun 7, 2017	Few-Shot Image Classificationgraph construction	CodeCode Available
A Baseline for Few-Shot Image Classification	Sep 6, 2019	ClassificationFew-Shot Image Classification	CodeCode Available
Subspace Adaptation Prior for Few-Shot Learning	Oct 13, 2023	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available
Low-Shot Learning for the Semantic Segmentation of Remote Sensing Imagery	Mar 26, 2018	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available
Visual Representation Learning with Self-Supervised Attention for Low-Label High-data Regime	Jan 22, 2022	Few-Shot Image Classificationimage-classification	CodeCode Available
Assessing Sample Quality via the Latent Space of Generative Models	Jul 21, 2024	Few-Shot Image Classificationimage-classification	CodeCode Available
Logarithm-transform aided Gaussian Sampling for Few-Shot Learning	Sep 28, 2023	ClassificationFew-Shot Image Classification	CodeCode Available
TADAM: Task dependent adaptive metric for improved few-shot learning	May 23, 2018	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available
Learning to Remember Rare Events	Mar 9, 2017	Few-Shot Image Classificationimage-classification	CodeCode Available
A Simple Neural Attentive Meta-Learner	Jul 11, 2017	Few-Shot Image ClassificationMeta-Learning	CodeCode Available
PRE: Vision-Language Prompt Learning with Reparameterization Encoder	Sep 14, 2023	Few-Shot Image ClassificationPrompt Engineering	CodeCode Available
Privacy Enhancement for Cloud-Based Few-Shot Learning	May 10, 2022	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available
RelationNet2: Deep Comparison Columns for Few-Shot Learning	Nov 17, 2018	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available
A Simple Approach to Adversarial Robustness in Few-shot Image Classification	Apr 11, 2022	Adversarial RobustnessFew-Shot Image Classification	CodeCode Available
Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning	May 25, 2018	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available
Learning to Propagate for Graph Meta-Learning	Sep 11, 2019	Dataset GenerationFew-Shot Image Classification	CodeCode Available
Learning to Obstruct Few-Shot Image Classification over Restricted Classes	Sep 28, 2024	AttributeClassification	CodeCode Available
Are LSTMs Good Few-Shot Learners?	Oct 22, 2023	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available
TextCaps : Handwritten Character Recognition with Very Small Datasets	Apr 17, 2019	Few-Shot Image ClassificationImage Classification	CodeCode Available
Learning to learn via Self-Critique	May 24, 2019	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available
Data-Efficient Language Shaped Few-shot Image Classification	Nov 1, 2021	ClassificationFew-Shot Image Classification	CodeCode Available
Curriculum Meta-Learning for Few-shot Classification	Dec 6, 2021	ClassificationFew-Shot Image Classification	CodeCode Available
Learning Deep Parsimonious Representations	Dec 1, 2016	ClusteringFew-Shot Image Classification	CodeCode Available
Knowledge Graph Transfer Network for Few-Shot Recognition	Nov 21, 2019	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available
RAFIC: Retrieval-Augmented Few-shot Image Classification	Dec 11, 2023	ClassificationFew-Shot Image Classification	CodeCode Available

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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