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 151–175 of 353 papers

TitleStatusHype
Few-Shot Action Recognition with Compromised Metric via Optimal Transport—0
A Closer Look at Prototype Classifier for Few-shot Image Classification—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
Multi-Domain Few-Shot Learning and Dataset for Agricultural Applications—0
Feature Aligning Few shot Learning Method Using Local Descriptors Weighted Rules—0
Feature Activation Map: Visual Explanation of Deep Learning Models for Image Classification—0
Generalized Adaptation for Few-Shot Learning—0
Confusable Learning for Large-class Few-Shot Classification—0
Exploring Category-correlated Feature for Few-shot Image Classification—0
Explore the Power of Dropout on Few-shot Learning—0
Compositional Few-Shot Recognition with Primitive Discovery and Enhancing—0
Model-Agnostic Meta-Learning for Multimodal Task Distributions—0
Modelling Multi-modal Cross-interaction for ML-FSIC Based on Local Feature Selection—0
Multi-scale Adaptive Task Attention Network for Few-Shot Learning—0
Exploiting Category Names for Few-Shot Classification with Vision-Language Models—0
CORL: Compositional Representation Learning for Few-Shot Classification—0
A Unified Framework with Meta-dropout for Few-shot Learning—0
Meta-OLE: Meta-learned Orthogonal Low-Rank Embedding—0
Enhancing Prototypical Few-Shot Learning by Leveraging the Local-Level Strategy—0
Enhancing Generalization of First-Order Meta-Learning—0
A Cognitive Paradigm Approach to Probe the Perception-Reasoning Interface in VLMs—0
Augmented Conditioning Is Enough For Effective Training Image Generation—0
argmax centroid—0
Class-Specific Channel Attention for Few-Shot Learning—0
Show:102550
← PrevPage 7 of 15Next →
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