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 251300 of 353 papers

TitleStatusHype
Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning0
Unsupervised Learning using Pretrained CNN and Associative Memory Bank0
Unsupervised Meta-Learning For Few-Shot Image Classification0
Variational Neuron Shifting for Few-Shot Image Classification Across Domains0
Variational Feature Disentangling for Fine-Grained Few-Shot Classification0
Visual-Semantic Contrastive Alignment for Few-Shot Image Classification0
Weakly-supervised Compositional FeatureAggregation for Few-shot Recognition0
How Does the Task Landscape Affect MAML Performance?0
Prototype Rectification for Few-Shot LearningCode0
Improving Few-Shot Visual Classification with Unlabelled ExamplesCode0
Cooperative Meta-Learning with Gradient AugmentationCode0
Tiny models from tiny data: Textual and null-text inversion for few-shot distillationCode0
Enhancing Few-Shot Image Classification with Unlabelled ExamplesCode0
Improved Few-Shot Visual ClassificationCode0
RAFIC: Retrieval-Augmented Few-shot Image ClassificationCode0
Image Deformation Meta-Networks for One-Shot LearningCode0
IDEA: Image Description Enhanced CLIP-AdapterCode0
iCAR: Bridging Image Classification and Image-text Alignment for Visual RecognitionCode0
Hyperspectral image classification via a random patches networkCode0
Hierarchically Structured Meta-learningCode0
Harnessing the Power of Infinitely Wide Deep Nets on Small-data TasksCode0
Gradient-Based Meta-Learning with Learned Layerwise Metric and SubspaceCode0
Revisiting Local Descriptor based Image-to-Class Measure for Few-shot LearningCode0
Towards a Neural StatisticianCode0
Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot TasksCode0
Contextualizing Meta-Learning via Learning to DecomposeCode0
Adversarially Robust Few-Shot Learning: A Meta-Learning ApproachCode0
Complementing Representation Deficiency in Few-shot Image Classification: A Meta-Learning ApproachCode0
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy ImitationCode0
Scaling Vision TransformersCode0
Few-Shot Learning with Global Class RepresentationsCode0
Few-Shot Learning for Image Classification of Common FloraCode0
CMVAE: Causal Meta VAE for Unsupervised Meta-LearningCode0
Few-Shot Image Recognition by Predicting Parameters from ActivationsCode0
Self-Supervised Learning For Few-Shot Image ClassificationCode0
Clustered Task-Aware Meta-Learning by Learning from Learning PathsCode0
ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark EvaluationCode0
Few and Fewer: Learning Better from Few Examples Using Fewer Base ClassesCode0
Associative Alignment for Few-shot Image ClassificationCode0
Graph-based Interpolation of Feature Vectors for Accurate Few-Shot ClassificationCode0
Assessing Sample Quality via the Latent Space of Generative ModelsCode0
Evaluation of Output Embeddings for Fine-Grained Image ClassificationCode0
SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for Few-shot Image ClassificationCode0
Edge-labeling Graph Neural Network for Few-shot LearningCode0
Adaptive Cross-Modal Few-Shot LearningCode0
Sign-MAML: Efficient Model-Agnostic Meta-Learning by SignSGDCode0
Diversity with Cooperation: Ensemble Methods for Few-Shot ClassificationCode0
DiffKendall: A Novel Approach for Few-Shot Learning with Differentiable Kendall's Rank CorrelationCode0
Simple Semi-supervised Knowledge Distillation from Vision-Language Models via Dual-Head OptimizationCode0
Delta-encoder: an effective sample synthesis method for few-shot object recognitionCode0
Show:102550
← PrevPage 6 of 8Next →

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+BPA (transductive)Accuracy85.59Unverified
6PT+MAP+SF+SOT (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+SOT (transductive)Accuracy91.34Unverified
9PT+MAP+SF+BPA (transductive)Accuracy91.34Unverified
10AmdimNetAccuracy90.98Unverified