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

Showing 176–200 of 353 papers

TitleStatusHype
Few-shot Image Classification based on Gradual Machine Learning—0
Few-Shot Image Classification via Contrastive Self-Supervised Learning—0
Few-shot Image Classification with Multi-Facet Prototypes—0
Few-Shot Learning as Domain Adaptation: Algorithm and Analysis—0
Few-Shot Learning of Compact Models via Task-Specific Meta Distillation—0
FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?—0
Frozen Feature Augmentation for Few-Shot Image Classification—0
GCCN: Global Context Convolutional Network—0
Generative Adversarial Networks Based on Transformer Encoder and Convolution Block for Hyperspectral Image Classification—0
Generative Adversarial Networks Based on Collaborative Learning and Attention Mechanism for Hyperspectral Image Classification—0
Geometric Mean Improves Loss For Few-Shot Learning—0
Gradient-EM Bayesian Meta-learning—0
Hierarchical Representation based Query-Specific Prototypical Network for Few-Shot Image Classification—0
How to distribute data across tasks for meta-learning?—0
HSI-BERT: Hyperspectral Image Classification Using the Bidirectional Encoder Representation From Transformers—0
Hyperspectral Image Classification of Convolutional Neural Network Combined with Valuable Samples—0
Impact of base dataset design on few-shot image classification—0
Improved Few-Shot Image Classification Through Multiple-Choice Questions—0
Improved Few-Shot Visual Classification—0
Improving Adversarially Robust Few-Shot Image Classification With Generalizable Representations—0
Improving Few-Shot Image Classification Using Machine- and User-Generated Natural Language Descriptions—0
Enhancing Few-Shot Image Classification with Unlabelled Examples—0
Improving Few-Shot Visual Classification with Unlabelled Examples—0
Improving Hyperbolic Representations via Gromov-Wasserstein Regularization—0
Inferring Prototypes for Multi-Label Few-Shot Image Classification with Word Vector Guided Attention—0
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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