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 126–150 of 353 papers

TitleStatusHype
Transductive Decoupled Variational Inference for Few-Shot ClassificationCode1
Cross-domain Few-shot Learning with Task-specific AdaptersCode1
Generalized Few-Shot Video Classification with Video Retrieval and Feature GenerationCode1
Frozen Feature Augmentation for Few-Shot Image Classification—0
FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?—0
Dataset Bias Prediction for Few-Shot Image Classification—0
Amortized Bayesian Meta-Learning—0
Few-Shot Learning of Compact Models via Task-Specific Meta Distillation—0
Matching Feature Sets for Few-Shot Image Classification—0
Cross-Modal Mapping: Mitigating the Modality Gap for Few-Shot Image Classification—0
Local descriptor-based multi-prototype network for few-shot Learning—0
Few-Shot Learning as Domain Adaptation: Algorithm and Analysis—0
Local Descriptors Weighted Adaptive Threshold Filtering For Few-Shot Learning—0
Few-shot Image Classification with Multi-Facet Prototypes—0
Few-Shot Image Classification via Contrastive Self-Supervised Learning—0
CovidExpert: A Triplet Siamese Neural Network framework for the detection of COVID-19—0
A Simple Task-aware Contrastive Local Descriptor Selection Strategy for Few-shot Learning between inter class and intra class—0
Machine learning with limited data—0
MDFL: A UNIFIED FRAMEWORK WITH META-DROPOUT FOR FEW-SHOT LEARNING—0
Few-shot Image Classification based on Gradual Machine Learning—0
Few-Shot Image Classification and Segmentation as Visual Question Answering Using Vision-Language Models—0
Continuous-Time Meta-Learning with Forward Mode Differentiation—0
Few-Shot Image Classification Along Sparse Graphs—0
Few-Shot Classification & Segmentation Using Large Language Models Agent—0
Baby steps towards few-shot learning with multiple semantics—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