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 251–300 of 353 papers

TitleStatusHype
Augmented Conditioning Is Enough For Effective Training Image Generation—0
Transfer Learning on Manifolds via Learned Transport Operators—0
Attribute Prototype Network for Any-Shot Learning—0
ProD: Prompting-To-Disentangle Domain Knowledge for Cross-Domain Few-Shot Image Classification—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
PrototypeFormer: Learning to Explore Prototype Relationships for Few-shot Image Classification—0
Asymmetric Distribution Measure for Few-shot Learning—0
Feature Aligning Few shot Learning Method Using Local Descriptors Weighted Rules—0
Prototypical Region Proposal Networks for Few-Shot Localization and Classification—0
Provably Improving Generalization of Few-Shot Models with Synthetic Data—0
Feature Activation Map: Visual Explanation of Deep Learning Models for Image Classification—0
Generalized Adaptation for Few-Shot Learning—0
Uncertainty-Aware Few-Shot Image Classification—0
Rapid Adaptation with Conditionally Shifted Neurons—0
Exploring Category-correlated Feature for Few-shot Image Classification—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
Uncertainty-based Network for Few-shot Image Classification—0
Revisiting Metric Learning for Few-Shot Image Classification—0
Weakly-supervised Compositional FeatureAggregation for Few-shot Recognition—0
Rich Semantics Improve Few-shot Learning—0
Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning—0
Robust High-dimensional Memory-augmented Neural Networks—0
RotoGBML: Towards Out-of-Distribution Generalization for Gradient-Based Meta-Learning—0
Assessing two novel distance-based loss functions for few-shot image classification—0
Distilling Self-Supervised Vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentation—0
Discriminative k-shot learning using probabilistic models—0
Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction—0
Deep Metric Learning for Few-Shot Image Classification: A Review of Recent Developments—0
A Simple Task-aware Contrastive Local Descriptor Selection Strategy for Few-shot Learning between inter class and intra class—0
Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification—0
Self-Supervised Prime-Dual Networks for Few-Shot Image Classification—0
DeepEMD: Few-Shot Image Classification With Differentiable Earth Mover's Distance and Structured Classifiers—0
Semantic Token Reweighting for Interpretable and Controllable Text Embeddings in CLIP—0
Dataset Bias Prediction for Few-Shot Image Classification—0
Semi-Supervised Risk Control via Prediction-Powered Inference—0
Cross-Modal Mapping: Mitigating the Modality Gap for Few-Shot Image Classification—0
argmax centroid—0
CovidExpert: A Triplet Siamese Neural Network framework for the detection of COVID-19—0
Siamese Transformer Networks for Few-shot Image Classification—0
An empirical study of pretrained representations for few-shot classification—0
Continuous-Time Meta-Learning with Forward Mode Differentiation—0
Confusable Learning for Large-class Few-Shot Classification—0
How Does the Task Landscape Affect MAML Performance?—0
Compositional Few-Shot Recognition with Primitive Discovery and Enhancing—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