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 201–250 of 353 papers

TitleStatusHype
InPK: Infusing Prior Knowledge into Prompt for Vision-Language Models—0
Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts—0
Knowledge-Aware Prompt Tuning for Generalizable Vision-Language Models—0
Layer-Wise Adaptive Updating for Few-Shot Image Classification—0
LDCA: Local Descriptors with Contextual Augmentation for Few-Shot Learning—0
Local descriptor-based multi-prototype network for few-shot Learning—0
Local Descriptors Weighted Adaptive Threshold Filtering For Few-Shot Learning—0
Machine learning with limited data—0
Matching Feature Sets for Few-Shot Image Classification—0
MDFL: A UNIFIED FRAMEWORK WITH META-DROPOUT FOR FEW-SHOT LEARNING—0
MetaGAN: An Adversarial Approach to Few-Shot Learning—0
Meta-OLE: Meta-learned Orthogonal Low-Rank Embedding—0
Model-Agnostic Graph Regularization for Few-Shot Learning—0
Model-Agnostic Meta-Learning for Multimodal Task Distributions—0
Modelling Multi-modal Cross-interaction for ML-FSIC Based on Local Feature Selection—0
Multi-Domain Few-Shot Learning and Dataset for Agricultural Applications—0
Multi-scale Adaptive Task Attention Network for Few-Shot Learning—0
Multi-Similarity Contrastive Learning—0
MVP: Meta Visual Prompt Tuning for Few-Shot Remote Sensing Image Scene Classification—0
NeurIPS'22 Cross-Domain MetaDL competition: Design and baseline results—0
Neuron Abandoning Attention Flow: Visual Explanation of Dynamics inside CNN Models—0
Object-Level Representation Learning for Few-Shot Image Classification—0
Ontology-based n-ball Concept Embeddings Informing Few-shot Image Classification—0
Optimal allocation of data across training tasks in meta-learning—0
Optimized Generic Feature Learning for Few-shot Classification across Domains—0
PAC-Bayes meta-learning with implicit task-specific posteriors—0
PaLI: A Jointly-Scaled Multilingual Language-Image Model—0
Partner-Assisted Learning for Few-Shot Image Classification—0
p-Meta: Towards On-device Deep Model Adaptation—0
Probabilistic Model-Agnostic Meta-Learning—0
ProD: Prompting-To-Disentangle Domain Knowledge for Cross-Domain Few-Shot Image Classification—0
PrototypeFormer: Learning to Explore Prototype Relationships for Few-shot Image Classification—0
Prototype Rectification for Few-Shot Learning—0
Prototypical Region Proposal Networks for Few-Shot Localization and Classification—0
Provably Improving Generalization of Few-Shot Models with Synthetic Data—0
Rapid Adaptation with Conditionally Shifted Neurons—0
Revisiting Metric Learning for Few-Shot Image Classification—0
Rich Semantics Improve Few-shot Learning—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
Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction—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
Semantic Token Reweighting for Interpretable and Controllable Text Embeddings in CLIP—0
Semi-Supervised Risk Control via Prediction-Powered Inference—0
Siamese Transformer Networks for Few-shot Image Classification—0
Spot and Learn: A Maximum-Entropy Patch Sampler for Few-Shot Image Classification—0
ST-MAML: A Stochastic-Task based Method for Task-Heterogeneous Meta-Learning—0
Strong Baselines for Parameter Efficient Few-Shot Fine-tuning—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