SOTAVerified

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

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Showing 301–325 of 353 papers

TitleStatusHype
A Cognitive Paradigm Approach to Probe the Perception-Reasoning Interface in VLMs—0
Unsupervised Meta-Learning For Few-Shot Image Classification—0
Class-Specific Channel Attention for Few-Shot Learning—0
Variational Neuron Shifting for Few-Shot Image Classification Across Domains—0
Spot and Learn: A Maximum-Entropy Patch Sampler for Few-Shot Image Classification—0
Class-relevant Patch Embedding Selection for Few-Shot Image Classification—0
Knowledge-Aware Prompt Tuning for Generalizable Vision-Language Models—0
Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts—0
InPK: Infusing Prior Knowledge into Prompt for Vision-Language Models—0
Inferring Prototypes for Multi-Label Few-Shot Image Classification with Word Vector Guided Attention—0
Improving Hyperbolic Representations via Gromov-Wasserstein Regularization—0
Layer-Wise Adaptive Updating for Few-Shot Image Classification—0
LDCA: Local Descriptors with Contextual Augmentation for Few-Shot Learning—0
ST-MAML: A Stochastic-Task based Method for Task-Heterogeneous Meta-Learning—0
Improving Few-Shot Visual Classification with Unlabelled Examples—0
Enhancing Few-Shot Image Classification with Unlabelled Examples—0
Variational Feature Disentangling for Fine-Grained Few-Shot Classification—0
Strong Baselines for Parameter Efficient Few-Shot Fine-tuning—0
A Closer Look at Prototype Classifier for Few-shot Image Classification—0
Subspace Representation Learning for Few-shot Image Classification—0
SuSana Distancia is all you need: Enforcing class separability in metric learning via two novel distance-based loss functions for few-shot image classification—0
Improving Few-Shot Image Classification Using Machine- and User-Generated Natural Language Descriptions—0
Improving Adversarially Robust Few-Shot Image Classification With Generalizable Representations—0
Improved Few-Shot Visual Classification—0
Improved Few-Shot Image Classification Through Multiple-Choice Questions—0
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All datasetsMini-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-shotCUB 200 5-way 5-shotFC100 5-way (1-shot)FC100 5-way (5-shot)Meta-DatasetOMNIGLOT - 1-Shot, 20-way

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SgVA-CLIPAccuracy97.95—Unverified
2CAML [Laion-2b]Accuracy96.2—Unverified
3P>M>F (P=DINO-ViT-base, M=ProtoNet)Accuracy95.3—Unverified
4TRIDENTAccuracy86.11—Unverified
5PT+MAP+SF+SOT (transductive)Accuracy85.59—Unverified
6PT+MAP+SF+BPA (transductive)Accuracy85.59—Unverified
7PEMnE-BMS* (transductive)Accuracy85.54—Unverified
8PT+MAP (s+f) (transductive)Accuracy84.81—Unverified
9BAVARDAGEAccuracy84.8—Unverified
10EASY 3xResNet12 (transductive)Accuracy84.04—Unverified
#ModelMetricClaimedVerifiedStatus
1SgVA-CLIPAccuracy98.72—Unverified
2CAML [Laion-2b]Accuracy98.6—Unverified
3P>M>F (P=DINO-ViT-base, M=ProtoNet)Accuracy98.4—Unverified
4TRIDENTAccuracy95.95—Unverified
5BAVARDAGEAccuracy91.65—Unverified
6PEMnE-BMS*(transductive)Accuracy91.53—Unverified
7Transductive CNAPS + FETIAccuracy91.5—Unverified
8PT+MAP+SF+BPA (transductive)Accuracy91.34—Unverified
9PT+MAP+SF+SOT (transductive)Accuracy91.34—Unverified
10AmdimNetAccuracy90.98—Unverified