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 101–150 of 353 papers

TitleStatusHype
Constellation Nets for Few-Shot LearningCode1
Transfer learning based few-shot classification using optimal transport mapping from preprocessed latent space of backbone neural networkCode1
Negative Margin Matters: Understanding Margin in Few-shot ClassificationCode1
Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated ExpertsCode1
Context-Aware Meta-LearningCode1
Prototype Completion for Few-Shot LearningCode1
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image ClassificationCode1
Worst Case Matters for Few-Shot RecognitionCode1
Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot ClassificationCode1
Channel Importance Matters in Few-Shot Image ClassificationCode1
Selecting Relevant Features from a Multi-domain Representation for Few-shot ClassificationCode1
Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple ClassifierCode1
MetaDelta: A Meta-Learning System for Few-shot Image ClassificationCode1
HyperShot: Few-Shot Learning by Kernel HyperNetworksCode1
BECLR: Batch Enhanced Contrastive Few-Shot LearningCode1
Match Them Up: Visually Explainable Few-shot Image ClassificationCode1
Few-Shot Learning by Integrating Spatial and Frequency RepresentationCode1
Matching Networks for One Shot LearningCode1
Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image ClassificationCode1
Memory Efficient Meta-Learning with Large ImagesCode1
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
Instance Credibility Inference for Few-Shot LearningCode1
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
A Closer Look at Prototype Classifier for Few-shot Image Classification—0
Few-Shot Learning of Compact Models via Task-Specific Meta Distillation—0
Cross-Modal Mapping: Mitigating the Modality Gap for Few-Shot Image Classification—0
Few-Shot Learning as Domain Adaptation: Algorithm and Analysis—0
Matching Feature Sets for Few-Shot Image Classification—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
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
Few-Shot Action Recognition with Compromised Metric via Optimal Transport—0
Feature Selection and Classification of Hyperspectral Images With Support Vector Machines—0
Feature Extraction of Hyperspectral Images With Image Fusion and Recursive Filtering—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