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 251–275 of 353 papers

TitleStatusHype
Uncertainty-Aware Few-Shot Image Classification—0
Uncertainty-based Network for Few-shot Image Classification—0
Probabilistic Model-Agnostic Meta-Learning—0
ProD: Prompting-To-Disentangle Domain Knowledge for Cross-Domain 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
Feature Aligning Few shot Learning Method Using Local Descriptors Weighted Rules—0
PrototypeFormer: Learning to Explore Prototype Relationships for Few-shot Image Classification—0
Prototype Rectification for Few-Shot Learning—0
Feature Activation Map: Visual Explanation of Deep Learning Models for Image Classification—0
Prototypical Region Proposal Networks for Few-Shot Localization and Classification—0
Provably Improving Generalization of Few-Shot Models with Synthetic Data—0
Generalized Adaptation for Few-Shot Learning—0
Exploring Category-correlated Feature for Few-shot Image Classification—0
Uncertainty in Model-Agnostic Meta-Learning using Variational Inference—0
Rapid Adaptation with Conditionally Shifted Neurons—0
Explore the Power of Dropout on Few-shot Learning—0
Exploiting Category Names for Few-Shot Classification with Vision-Language Models—0
Enhancing Prototypical Few-Shot Learning by Leveraging the Local-Level Strategy—0
Enhancing Generalization of First-Order Meta-Learning—0
Divergent Search for Few-Shot Image Classification—0
Distilling Self-Supervised Vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentation—0
Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning—0
Revisiting Metric Learning for Few-Shot Image Classification—0
A Simple Task-aware Contrastive Local Descriptor Selection Strategy for Few-shot Learning between inter class and intra class—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