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 276–300 of 353 papers

TitleStatusHype
Rich Semantics Improve Few-shot Learning—0
argmax centroid—0
Robust High-dimensional Memory-augmented Neural Networks—0
RotoGBML: Towards Out-of-Distribution Generalization for Gradient-Based Meta-Learning—0
Scaling Vision Transformers—0
Discriminative k-shot learning using probabilistic models—0
Deep Metric Learning for Few-Shot Image Classification: A Review of Recent Developments—0
Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction—0
DeepEMD: Few-Shot Image Classification With Differentiable Earth Mover's Distance and Structured Classifiers—0
An empirical study of pretrained representations for few-shot classification—0
Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification—0
Self-Supervised Prime-Dual Networks for Few-Shot Image Classification—0
Dataset Bias Prediction for Few-Shot Image Classification—0
Semantic Token Reweighting for Interpretable and Controllable Text Embeddings in CLIP—0
Cross-Modal Mapping: Mitigating the Modality Gap for Few-Shot Image Classification—0
Semi-Supervised Risk Control via Prediction-Powered Inference—0
CovidExpert: A Triplet Siamese Neural Network framework for the detection of COVID-19—0
Amortized Bayesian Meta-Learning—0
Continuous-Time Meta-Learning with Forward Mode Differentiation—0
Siamese Transformer Networks for Few-shot Image Classification—0
How Does the Task Landscape Affect MAML Performance?—0
Confusable Learning for Large-class Few-Shot Classification—0
Compositional Few-Shot Recognition with Primitive Discovery and Enhancing—0
Unsupervised Learning using Pretrained CNN and Associative Memory Bank—0
CORL: Compositional Representation Learning for Few-Shot Classification—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+BPA (transductive)Accuracy85.59—Unverified
6PT+MAP+SF+SOT (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+SOT (transductive)Accuracy91.34—Unverified
9PT+MAP+SF+BPA (transductive)Accuracy91.34—Unverified
10AmdimNetAccuracy90.98—Unverified