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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 326–350 of 353 papers

TitleStatusHype
Local descriptor-based multi-prototype network for few-shot Learning—0
Local Descriptors Weighted Adaptive Threshold Filtering For Few-Shot Learning—0
Visual-Semantic Contrastive Alignment for Few-Shot Image Classification—0
TALDS-Net: Task-Aware Adaptive Local Descriptors Selection for Few-shot Image Classification—0
Semantic Regularization: Improve Few-shot Image Classification by Reducing Meta Shift—0
Task-Prior Conditional Variational Auto-Encoder for Few-Shot Image Classification—0
Machine learning with limited data—0
Matching Feature Sets for Few-Shot Image Classification—0
Impact of base dataset design on few-shot image classification—0
Hyperspectral Image Classification of Convolutional Neural Network Combined with Valuable Samples—0
Task-Specific Preconditioner for Cross-Domain Few-Shot Learning—0
MDFL: A UNIFIED FRAMEWORK WITH META-DROPOUT FOR FEW-SHOT LEARNING—0
HSI-BERT: Hyperspectral Image Classification Using the Bidirectional Encoder Representation From Transformers—0
Yet Meta Learning Can Adapt Fast, It Can Also Break Easily—0
How to distribute data across tasks for meta-learning?—0
Centroid-based deep metric learning for speaker recognition—0
Hierarchical Representation based Query-Specific Prototypical Network for Few-Shot Image Classification—0
Gradient-EM Bayesian Meta-learning—0
Brain Inspired Adaptive Memory Dual-Net for Few-Shot Image Classification—0
MetaGAN: An Adversarial Approach to Few-Shot Learning—0
Boosting Few-Shot Text Classification via Distribution Estimation—0
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification—0
Toward Multimodal Model-Agnostic Meta-Learning—0
Weakly-supervised Compositional FeatureAggregation for Few-shot Recognition—0
Geometric Mean Improves Loss For Few-Shot Learning—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