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 151–200 of 353 papers

TitleStatusHype
Confusable Learning for Large-class Few-Shot Classification—0
Continuous-Time Meta-Learning with Forward Mode Differentiation—0
CovidExpert: A Triplet Siamese Neural Network framework for the detection of COVID-19—0
Cross-Modal Mapping: Mitigating the Modality Gap for Few-Shot Image Classification—0
Dataset Bias Prediction for Few-Shot Image Classification—0
DeepEMD: Few-Shot Image Classification With Differentiable Earth Mover's Distance and Structured Classifiers—0
Deep Metric Learning for Few-Shot Image Classification: A Review of Recent Developments—0
Discriminative k-shot learning using probabilistic models—0
Distilling Self-Supervised Vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentation—0
Divergent Search for Few-Shot Image Classification—0
Enhancing Generalization of First-Order Meta-Learning—0
Enhancing Prototypical Few-Shot Learning by Leveraging the Local-Level Strategy—0
Exploiting Category Names for Few-Shot Classification with Vision-Language Models—0
Explore the Power of Dropout on Few-shot Learning—0
Exploring Category-correlated Feature for Few-shot Image Classification—0
Generalized Adaptation for Few-Shot Learning—0
Feature Activation Map: Visual Explanation of Deep Learning Models for Image Classification—0
Feature Aligning Few shot Learning Method Using Local Descriptors Weighted Rules—0
Feature Extraction of Hyperspectral Images With Image Fusion and Recursive Filtering—0
Feature Selection and Classification of Hyperspectral Images With Support Vector Machines—0
Few-Shot Action Recognition with Compromised Metric via Optimal Transport—0
Few-Shot Classification & Segmentation Using Large Language Models Agent—0
Few-Shot Image Classification Along Sparse Graphs—0
Few-Shot Image Classification and Segmentation as Visual Question Answering Using Vision-Language Models—0
Few-shot Image Classification based on Gradual Machine Learning—0
Few-Shot Image Classification via Contrastive Self-Supervised Learning—0
Few-shot Image Classification with Multi-Facet Prototypes—0
Few-Shot Learning as Domain Adaptation: Algorithm and Analysis—0
Few-Shot Learning of Compact Models via Task-Specific Meta Distillation—0
FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?—0
Frozen Feature Augmentation for Few-Shot Image Classification—0
GCCN: Global Context Convolutional Network—0
Generative Adversarial Networks Based on Transformer Encoder and Convolution Block for Hyperspectral Image Classification—0
Generative Adversarial Networks Based on Collaborative Learning and Attention Mechanism for Hyperspectral Image Classification—0
Geometric Mean Improves Loss For Few-Shot Learning—0
Gradient-EM Bayesian Meta-learning—0
Hierarchical Representation based Query-Specific Prototypical Network for Few-Shot Image Classification—0
How to distribute data across tasks for meta-learning?—0
HSI-BERT: Hyperspectral Image Classification Using the Bidirectional Encoder Representation From Transformers—0
Hyperspectral Image Classification of Convolutional Neural Network Combined with Valuable Samples—0
Impact of base dataset design on few-shot image classification—0
Improved Few-Shot Image Classification Through Multiple-Choice Questions—0
Improving Adversarially Robust Few-Shot Image Classification With Generalizable Representations—0
Improving Few-Shot Image Classification Using Machine- and User-Generated Natural Language Descriptions—0
Improving Hyperbolic Representations via Gromov-Wasserstein Regularization—0
Inferring Prototypes for Multi-Label Few-Shot Image Classification with Word Vector Guided Attention—0
InPK: Infusing Prior Knowledge into Prompt for Vision-Language Models—0
Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts—0
Knowledge-Aware Prompt Tuning for Generalizable Vision-Language Models—0
Layer-Wise Adaptive Updating for Few-Shot Image Classification—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