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 251–300 of 353 papers

TitleStatusHype
Subspace Representation Learning for Few-shot Image Classification—0
SuSana Distancia is all you need: Enforcing class separability in metric learning via two novel distance-based loss functions for few-shot image classification—0
TALDS-Net: Task-Aware Adaptive Local Descriptors Selection for Few-shot Image Classification—0
Task-Prior Conditional Variational Auto-Encoder for Few-Shot Image Classification—0
Task-Specific Preconditioner for Cross-Domain Few-Shot Learning—0
Toward Multimodal Model-Agnostic Meta-Learning—0
Transfer Learning on Manifolds via Learned Transport Operators—0
Uncertainty-Aware Few-Shot Image Classification—0
Uncertainty-based Network for Few-shot Image Classification—0
Uncertainty in Model-Agnostic Meta-Learning using Variational Inference—0
Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning—0
Unsupervised Learning using Pretrained CNN and Associative Memory Bank—0
Unsupervised Meta-Learning For Few-Shot Image Classification—0
Variational Neuron Shifting for Few-Shot Image Classification Across Domains—0
Variational Feature Disentangling for Fine-Grained Few-Shot Classification—0
Visual-Semantic Contrastive Alignment for Few-Shot Image Classification—0
Weakly-supervised Compositional FeatureAggregation for Few-shot Recognition—0
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
Unsupervised Image Classification for Deep Representation LearningCode0
Adversarially Robust Few-Shot Learning: A Meta-Learning ApproachCode0
Towards a Neural StatisticianCode0
Cooperative Meta-Learning with Gradient AugmentationCode0
Contextualizing Meta-Learning via Learning to DecomposeCode0
Gradient-Based Meta-Learning with Learned Layerwise Metric and SubspaceCode0
Few-Shot Learning with Global Class RepresentationsCode0
Complementing Representation Deficiency in Few-shot Image Classification: A Meta-Learning ApproachCode0
Few-Shot Learning for Image Classification of Common FloraCode0
Self-Supervised Learning For Few-Shot Image ClassificationCode0
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy ImitationCode0
CMVAE: Causal Meta VAE for Unsupervised Meta-LearningCode0
Few-Shot Image Recognition by Predicting Parameters from ActivationsCode0
Image Deformation Meta-Networks for One-Shot LearningCode0
Few and Fewer: Learning Better from Few Examples Using Fewer Base ClassesCode0
Clustered Task-Aware Meta-Learning by Learning from Learning PathsCode0
Graph-based Interpolation of Feature Vectors for Accurate Few-Shot ClassificationCode0
SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for Few-shot Image ClassificationCode0
Evaluation of Output Embeddings for Fine-Grained Image ClassificationCode0
Associative Alignment for Few-shot Image ClassificationCode0
Sign-MAML: Efficient Model-Agnostic Meta-Learning by SignSGDCode0
Meta-Learning Probabilistic Inference For PredictionCode0
Meta-Learning without MemorizationCode0
Edge-labeling Graph Neural Network for Few-shot LearningCode0
Meta-Learning Initializations for Image SegmentationCode0
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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