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 201–250 of 353 papers

TitleStatusHype
Sign-MAML: Efficient Model-Agnostic Meta-Learning by SignSGDCode0
Simple Semi-supervised Knowledge Distillation from Vision-Language Models via Dual-Head OptimizationCode0
Small Sample Hyperspectral Image Classification Based on the Random Patches Network and Recursive FilteringCode0
Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural NetworkCode0
Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-LearningCode0
Subspace Adaptation Prior for Few-Shot LearningCode0
TADAM: Task dependent adaptive metric for improved few-shot learningCode0
TextCaps : Handwritten Character Recognition with Very Small DatasetsCode0
Tiny models from tiny data: Textual and null-text inversion for few-shot distillationCode0
Towards a Neural StatisticianCode0
Unsupervised Image Classification for Deep Representation LearningCode0
Visual Representation Learning with Self-Supervised Attention for Low-Label High-data RegimeCode0
ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark EvaluationCode0
Meta-OLE: Meta-learned Orthogonal Low-Rank Embedding—0
GCCN: Global Context Convolutional Network—0
Model-Agnostic Graph Regularization for Few-Shot Learning—0
Frozen Feature Augmentation for Few-Shot Image Classification—0
Model-Agnostic Meta-Learning for Multimodal Task Distributions—0
Modelling Multi-modal Cross-interaction for ML-FSIC Based on Local Feature Selection—0
Baby steps towards few-shot learning with multiple semantics—0
Multi-Domain Few-Shot Learning and Dataset for Agricultural Applications—0
A Unified Framework with Meta-dropout for Few-shot Learning—0
Augmented Conditioning Is Enough For Effective Training Image Generation—0
Attribute Prototype Network for Any-Shot Learning—0
FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?—0
Multi-scale Adaptive Task Attention Network for Few-Shot Learning—0
Multi-Similarity Contrastive Learning—0
Few-Shot Learning of Compact Models via Task-Specific Meta Distillation—0
Transfer Learning on Manifolds via Learned Transport Operators—0
MVP: Meta Visual Prompt Tuning for Few-Shot Remote Sensing Image Scene Classification—0
Few-Shot Learning as Domain Adaptation: Algorithm and Analysis—0
Asymmetric Distribution Measure for Few-shot Learning—0
NeurIPS'22 Cross-Domain MetaDL competition: Design and baseline results—0
Neuron Abandoning Attention Flow: Visual Explanation of Dynamics inside CNN Models—0
Object-Level Representation Learning 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
Assessing two novel distance-based loss functions for few-shot image classification—0
Few-shot Image Classification based on Gradual Machine Learning—0
Ontology-based n-ball Concept Embeddings Informing Few-shot Image Classification—0
Few-Shot Image Classification and Segmentation as Visual Question Answering Using Vision-Language Models—0
Optimal allocation of data across training tasks in meta-learning—0
Optimized Generic Feature Learning for Few-shot Classification across Domains—0
Few-Shot Image Classification Along Sparse Graphs—0
PAC-Bayes meta-learning with implicit task-specific posteriors—0
PaLI: A Jointly-Scaled Multilingual Language-Image Model—0
Few-Shot Classification & Segmentation Using Large Language Models Agent—0
Partner-Assisted Learning for Few-Shot Image Classification—0
Few-Shot Action Recognition with Compromised Metric via Optimal Transport—0
p-Meta: Towards On-device Deep Model Adaptation—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