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 101150 of 353 papers

TitleStatusHype
Constellation Nets for Few-Shot LearningCode1
Universal Representation Learning from Multiple Domains for Few-shot ClassificationCode1
Prompt Tuning based Adapter for Vision-Language Model AdaptionCode1
Meta-Learning with Implicit GradientsCode1
Context-Aware Meta-LearningCode1
VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue DistributionCode1
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image ClassificationCode1
Model-Agnostic Meta-Learning for Fast Adaptation of Deep NetworksCode1
Channel Importance Matters in Few-Shot Image ClassificationCode1
Diffusion Mechanism in Residual Neural Network: Theory and ApplicationsCode1
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
Multistage Relation Network With Dual-Metric for Few-Shot Hyperspectral Image ClassificationCode1
On First-Order Meta-Learning AlgorithmsCode1
BECLR: Batch Enhanced Contrastive Few-Shot LearningCode1
Negative Margin Matters: Understanding Margin in Few-shot ClassificationCode1
Few-Shot Learning by Integrating Spatial and Frequency RepresentationCode1
Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective AdaptationCode1
Region Comparison Network for Interpretable Few-shot Image ClassificationCode1
On sensitivity of meta-learning to support dataCode1
Shallow Bayesian Meta Learning for Real-World Few-Shot RecognitionCode1
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
Transformers Can Do Bayesian InferenceCode1
Instance Credibility Inference for Few-Shot LearningCode1
Generalized Few-Shot Video Classification with Video Retrieval and Feature GenerationCode1
On the Efficacy of Differentially Private Few-shot Image ClassificationCode0
Assessing Sample Quality via the Latent Space of Generative ModelsCode0
Data-Efficient Language Shaped Few-shot Image ClassificationCode0
Few-Shot Learning with Global Class RepresentationsCode0
Curriculum Meta-Learning for Few-shot ClassificationCode0
Few-Shot Learning for Image Classification of Common FloraCode0
Support-Set Context Matters for Bongard ProblemsCode0
Few-Shot Image Recognition by Predicting Parameters from ActivationsCode0
Neural TMDlayer: Modeling Instantaneous flow of features via SDE GeneratorsCode0
Image Deformation Meta-Networks for One-Shot LearningCode0
Cooperative Meta-Learning with Gradient AugmentationCode0
Simultaneous Perturbation Method for Multi-Task Weight Optimization in One-Shot Meta-LearningCode0
Multidimensional Belief Quantification for Label-Efficient Meta-LearningCode0
A Simple Neural Attentive Meta-LearnerCode0
Multi-Level Correlation Network For Few-Shot Image ClassificationCode0
A Simple Approach to Adversarial Robustness in Few-shot Image ClassificationCode0
Multi-level Metric Learning for Few-shot Image RecognitionCode0
Few and Fewer: Learning Better from Few Examples Using Fewer Base ClassesCode0
Meta-Learning without MemorizationCode0
Contextualizing Meta-Learning via Learning to DecomposeCode0
Multi-level Second-order Few-shot LearningCode0
Meta-Learning Initializations for Image SegmentationCode0
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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