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 176200 of 353 papers

TitleStatusHype
Few-Shot Image Classification via Contrastive Self-Supervised Learning0
Few-shot Image Classification with Multi-Facet Prototypes0
Few-Shot Learning as Domain Adaptation: Algorithm and Analysis0
Few-Shot Learning of Compact Models via Task-Specific Meta Distillation0
FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?0
Frozen Feature Augmentation for Few-Shot Image Classification0
GCCN: Global Context Convolutional Network0
Generative Adversarial Networks Based on Transformer Encoder and Convolution Block for Hyperspectral Image Classification0
Generative Adversarial Networks Based on Collaborative Learning and Attention Mechanism for Hyperspectral Image Classification0
Geometric Mean Improves Loss For Few-Shot Learning0
Gradient-EM Bayesian Meta-learning0
Hierarchical Representation based Query-Specific Prototypical Network for Few-Shot Image Classification0
How to distribute data across tasks for meta-learning?0
HSI-BERT: Hyperspectral Image Classification Using the Bidirectional Encoder Representation From Transformers0
Hyperspectral Image Classification of Convolutional Neural Network Combined with Valuable Samples0
Impact of base dataset design on few-shot image classification0
Improved Few-Shot Image Classification Through Multiple-Choice Questions0
Improving Adversarially Robust Few-Shot Image Classification With Generalizable Representations0
Improving Few-Shot Image Classification Using Machine- and User-Generated Natural Language Descriptions0
Improving Hyperbolic Representations via Gromov-Wasserstein Regularization0
Inferring Prototypes for Multi-Label Few-Shot Image Classification with Word Vector Guided Attention0
InPK: Infusing Prior Knowledge into Prompt for Vision-Language Models0
Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts0
Knowledge-Aware Prompt Tuning for Generalizable Vision-Language Models0
Layer-Wise Adaptive Updating for Few-Shot Image Classification0
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Benchmark Results

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