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 251275 of 353 papers

TitleStatusHype
Subspace Representation Learning for Few-shot Image Classification0
SuSana Distancia is all you need: Enforcing class separability in metric learning via two novel distance-based loss functions for few-shot image classification0
TALDS-Net: Task-Aware Adaptive Local Descriptors Selection for Few-shot Image Classification0
Task-Prior Conditional Variational Auto-Encoder for Few-Shot Image Classification0
Task-Specific Preconditioner for Cross-Domain Few-Shot Learning0
Toward Multimodal Model-Agnostic Meta-Learning0
Transfer Learning on Manifolds via Learned Transport Operators0
Uncertainty-Aware Few-Shot Image Classification0
Uncertainty-based Network for Few-shot Image Classification0
Uncertainty in Model-Agnostic Meta-Learning using Variational Inference0
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
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
Revisiting Local Descriptor based Image-to-Class Measure for Few-shot LearningCode0
Tiny models from tiny data: Textual and null-text inversion for few-shot distillationCode0
Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot TasksCode0
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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+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