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 126150 of 353 papers

TitleStatusHype
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 Classification0
FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?0
Dataset Bias Prediction for Few-Shot Image Classification0
A Closer Look at Prototype Classifier for Few-shot Image Classification0
Few-Shot Learning of Compact Models via Task-Specific Meta Distillation0
Cross-Modal Mapping: Mitigating the Modality Gap for Few-Shot Image Classification0
Few-Shot Learning as Domain Adaptation: Algorithm and Analysis0
Matching Feature Sets for Few-Shot Image Classification0
Few-shot Image Classification with Multi-Facet Prototypes0
Few-Shot Image Classification via Contrastive Self-Supervised Learning0
CovidExpert: A Triplet Siamese Neural Network framework for the detection of COVID-190
A Simple Task-aware Contrastive Local Descriptor Selection Strategy for Few-shot Learning between inter class and intra class0
MDFL: A UNIFIED FRAMEWORK WITH META-DROPOUT FOR FEW-SHOT LEARNING0
Few-shot Image Classification based on Gradual Machine Learning0
Few-Shot Image Classification and Segmentation as Visual Question Answering Using Vision-Language Models0
Continuous-Time Meta-Learning with Forward Mode Differentiation0
Few-Shot Image Classification Along Sparse Graphs0
Few-Shot Classification & Segmentation Using Large Language Models Agent0
Baby steps towards few-shot learning with multiple semantics0
Few-Shot Action Recognition with Compromised Metric via Optimal Transport0
Feature Selection and Classification of Hyperspectral Images With Support Vector Machines0
Feature Extraction of Hyperspectral Images With Image Fusion and Recursive Filtering0
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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