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 5175 of 353 papers

TitleStatusHype
Enhancing Few-shot Image Classification with Cosine TransformerCode1
Context-Aware Meta-LearningCode1
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image ClassificationCode1
DeepEMD: Differentiable Earth Mover's Distance for Few-Shot LearningCode1
BaseTransformers: Attention over base data-points for One Shot LearningCode1
Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive ProcessesCode1
BECLR: Batch Enhanced Contrastive Few-Shot LearningCode1
Match Them Up: Visually Explainable Few-shot Image ClassificationCode1
Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image ClassificationCode1
Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot LearningCode1
Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object InteractionsCode1
Debiased Learning from Naturally Imbalanced Pseudo-LabelsCode1
Distilling Large Vision-Language Model with Out-of-Distribution GeneralizabilityCode1
Dynamic Few-Shot Visual Learning without ForgettingCode1
Few-Shot Image Classification Benchmarks are Too Far From Reality: Build Back Better with Semantic Task SamplingCode1
Bayesian Meta-Learning for the Few-Shot Setting via Deep KernelsCode1
Diversified in-domain synthesis with efficient fine-tuning for few-shot classificationCode1
Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple ClassifierCode1
Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective AdaptationCode1
Few-shot Relational Reasoning via Connection Subgraph PretrainingCode1
A Broader Study of Cross-Domain Few-Shot LearningCode1
Diffusion Mechanism in Residual Neural Network: Theory and ApplicationsCode1
Channel Importance Matters in Few-Shot Image ClassificationCode1
Disentangled Feature Representation for Few-shot Image ClassificationCode1
How to train your MAMLCode1
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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