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 301–350 of 353 papers

TitleStatusHype
Knowledge-Aware Prompt Tuning for Generalizable Vision-Language Models—0
Layer-Wise Adaptive Updating for Few-Shot Image Classification—0
LDCA: Local Descriptors with Contextual Augmentation for Few-Shot Learning—0
CORL: Compositional Representation Learning for Few-Shot Classification—0
Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts—0
InPK: Infusing Prior Knowledge into Prompt for Vision-Language Models—0
Unsupervised Learning using Pretrained CNN and Associative Memory Bank—0
A Cognitive Paradigm Approach to Probe the Perception-Reasoning Interface in VLMs—0
Unsupervised Meta-Learning For Few-Shot Image Classification—0
Spot and Learn: A Maximum-Entropy Patch Sampler for Few-Shot Image Classification—0
Class-Specific Channel Attention for Few-Shot Learning—0
Inferring Prototypes for Multi-Label Few-Shot Image Classification with Word Vector Guided Attention—0
Improving Hyperbolic Representations via Gromov-Wasserstein Regularization—0
Improving Few-Shot Image Classification Using Machine- and User-Generated Natural Language Descriptions—0
Improving Adversarially Robust Few-Shot Image Classification With Generalizable Representations—0
Local descriptor-based multi-prototype network for few-shot Learning—0
Local Descriptors Weighted Adaptive Threshold Filtering For Few-Shot Learning—0
ST-MAML: A Stochastic-Task based Method for Task-Heterogeneous Meta-Learning—0
Variational Neuron Shifting for Few-Shot Image Classification Across Domains—0
Strong Baselines for Parameter Efficient Few-Shot Fine-tuning—0
Variational Feature Disentangling for Fine-Grained Few-Shot Classification—0
Machine learning with limited data—0
Matching Feature Sets for Few-Shot Image Classification—0
Improved Few-Shot Image Classification Through Multiple-Choice Questions—0
Impact of base dataset design on few-shot image classification—0
Subspace Representation Learning for Few-shot Image Classification—0
MDFL: A UNIFIED FRAMEWORK WITH META-DROPOUT FOR FEW-SHOT LEARNING—0
Hyperspectral Image Classification of Convolutional Neural Network Combined with Valuable Samples—0
SuSana Distancia is all you need: Enforcing class separability in metric learning via two novel distance-based loss functions for few-shot image classification—0
HSI-BERT: Hyperspectral Image Classification Using the Bidirectional Encoder Representation From Transformers—0
A Closer Look at Prototype Classifier for Few-shot Image Classification—0
How to distribute data across tasks for meta-learning?—0
Hierarchical Representation based Query-Specific Prototypical Network for Few-Shot Image Classification—0
TALDS-Net: Task-Aware Adaptive Local Descriptors Selection for Few-shot Image Classification—0
MetaGAN: An Adversarial Approach to Few-Shot Learning—0
Class-relevant Patch Embedding Selection for Few-Shot Image Classification—0
Task-Prior Conditional Variational Auto-Encoder for Few-Shot Image Classification—0
Task-Specific Preconditioner for Cross-Domain Few-Shot Learning—0
Visual-Semantic Contrastive Alignment for Few-Shot Image Classification—0
Gradient-EM Bayesian Meta-learning—0
Geometric Mean Improves Loss For Few-Shot Learning—0
Generative Adversarial Networks Based on Collaborative Learning and Attention Mechanism for Hyperspectral Image Classification—0
Semantic Regularization: Improve Few-shot Image Classification by Reducing Meta Shift—0
Meta-OLE: Meta-learned Orthogonal Low-Rank Embedding—0
Generative Adversarial Networks Based on Transformer Encoder and Convolution Block for Hyperspectral Image Classification—0
Model-Agnostic Graph Regularization for Few-Shot Learning—0
GCCN: Global Context Convolutional Network—0
Model-Agnostic Meta-Learning for Multimodal Task Distributions—0
Modelling Multi-modal Cross-interaction for ML-FSIC Based on Local Feature Selection—0
Centroid-based deep metric learning for speaker recognition—0
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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