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 101–150 of 353 papers

TitleStatusHype
Constellation Nets for Few-Shot LearningCode1
Transductive Information Maximization For Few-Shot LearningCode1
MetaDelta: A Meta-Learning System for Few-shot Image ClassificationCode1
Multimodal Model-Agnostic Meta-Learning via Task-Aware ModulationCode1
Context-Aware Meta-LearningCode1
Tree Structure-Aware Few-Shot Image Classification via Hierarchical AggregationCode1
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image ClassificationCode1
Unsupervised Embedding Adaptation via Early-Stage Feature Reconstruction for Few-Shot ClassificationCode1
VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue DistributionCode1
Worst Case Matters for Few-Shot RecognitionCode1
Few-Shot Image Classification Benchmarks are Too Far From Reality: Build Back Better with Semantic Task SamplingCode1
Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple ClassifierCode1
Proto-CLIP: Vision-Language Prototypical Network for Few-Shot LearningCode1
Instance Credibility Inference for Few-Shot LearningCode1
BECLR: Batch Enhanced Contrastive Few-Shot LearningCode1
Selecting Relevant Features from a Multi-domain Representation for Few-shot ClassificationCode1
Few-Shot Learning by Integrating Spatial and Frequency RepresentationCode1
How to train your MAMLCode1
Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image ClassificationCode1
Matching Networks for One Shot LearningCode1
Few-shot Relational Reasoning via Connection Subgraph PretrainingCode1
FewVS: A Vision-Semantics Integration Framework for Few-Shot Image ClassificationCode1
Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object InteractionsCode1
Few-Shot Classification with Feature Map Reconstruction NetworksCode1
Debiased Learning from Naturally Imbalanced Pseudo-LabelsCode1
Transductive Decoupled Variational Inference for Few-Shot ClassificationCode1
Cross-domain Few-shot Learning with Task-specific AdaptersCode1
Generalized Few-Shot Video Classification with Video Retrieval and Feature GenerationCode1
Frozen Feature Augmentation for Few-Shot Image Classification—0
FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?—0
Dataset Bias Prediction for Few-Shot Image Classification—0
Amortized Bayesian Meta-Learning—0
Few-Shot Learning of Compact Models via Task-Specific Meta Distillation—0
Matching Feature Sets for Few-Shot Image Classification—0
Cross-Modal Mapping: Mitigating the Modality Gap for Few-Shot Image Classification—0
Local descriptor-based multi-prototype network for few-shot Learning—0
Few-Shot Learning as Domain Adaptation: Algorithm and Analysis—0
Local Descriptors Weighted Adaptive Threshold Filtering For Few-Shot Learning—0
Few-shot Image Classification with Multi-Facet Prototypes—0
Few-Shot Image Classification via Contrastive Self-Supervised Learning—0
CovidExpert: A Triplet Siamese Neural Network framework for the detection of COVID-19—0
A Simple Task-aware Contrastive Local Descriptor Selection Strategy for Few-shot Learning between inter class and intra class—0
Machine learning with limited data—0
MDFL: A UNIFIED FRAMEWORK WITH META-DROPOUT FOR FEW-SHOT LEARNING—0
Few-shot Image Classification based on Gradual Machine Learning—0
Few-Shot Image Classification and Segmentation as Visual Question Answering Using Vision-Language Models—0
Continuous-Time Meta-Learning with Forward Mode Differentiation—0
Few-Shot Image Classification Along Sparse Graphs—0
Few-Shot Classification & Segmentation Using Large Language Models Agent—0
Baby steps towards few-shot learning with multiple semantics—0
Show:102550
← PrevPage 3 of 8Next →
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