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 1–50 of 353 papers

TitleStatusHype
ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark EvaluationCode0
Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts—0
Provably Improving Generalization of Few-Shot Models with Synthetic Data—0
Simple Semi-supervised Knowledge Distillation from Vision-Language Models via Dual-Head OptimizationCode0
Brain Inspired Adaptive Memory Dual-Net for Few-Shot Image Classification—0
InPK: Infusing Prior Knowledge into Prompt for Vision-Language Models—0
Augmented Conditioning Is Enough For Effective Training Image Generation—0
Geometric Mean Improves Loss For Few-Shot Learning—0
A Cognitive Paradigm Approach to Probe the Perception-Reasoning Interface in VLMs—0
IDEA: Image Description Enhanced CLIP-AdapterCode0
Cross-Modal Mapping: Mitigating the Modality Gap for Few-Shot Image Classification—0
Task-Specific Preconditioner for Cross-Domain Few-Shot Learning—0
Modelling Multi-modal Cross-interaction for ML-FSIC Based on Local Feature Selection—0
Semi-Supervised Risk Control via Prediction-Powered Inference—0
Multi-Level Correlation Network For Few-Shot Image ClassificationCode0
Neuron Abandoning Attention Flow: Visual Explanation of Dynamics inside CNN Models—0
Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification—0
FewVS: A Vision-Semantics Integration Framework for Few-Shot Image ClassificationCode1
Semantic Token Reweighting for Interpretable and Controllable Text Embeddings in CLIP—0
Learning to Obstruct Few-Shot Image Classification over Restricted ClassesCode0
Enhancing Few-Shot Image Classification through Learnable Multi-Scale Embedding and Attention MechanismsCode1
Local Descriptors Weighted Adaptive Threshold Filtering For Few-Shot Learning—0
Feature Aligning Few shot Learning Method Using Local Descriptors Weighted Rules—0
A Simple Task-aware Contrastive Local Descriptor Selection Strategy for Few-shot Learning between inter class and intra class—0
Improved Few-Shot Image Classification Through Multiple-Choice Questions—0
Assessing Sample Quality via the Latent Space of Generative ModelsCode0
Siamese Transformer Networks for Few-shot Image Classification—0
Improving Hyperbolic Representations via Gromov-Wasserstein Regularization—0
AWT: Transferring Vision-Language Models via Augmentation, Weighting, and TransportationCode2
GalLoP: Learning Global and Local Prompts for Vision-Language ModelsCode2
The Balanced-Pairwise-Affinities Feature TransformCode2
Cooperative Meta-Learning with Gradient AugmentationCode0
Tiny models from tiny data: Textual and null-text inversion for few-shot distillationCode0
Class-relevant Patch Embedding Selection for Few-Shot Image Classification—0
Variational Neuron Shifting for Few-Shot Image Classification Across Domains—0
Leveraging Cross-Modal Neighbor Representation for Improved CLIP ClassificationCode1
Frozen Feature Augmentation for Few-Shot Image Classification—0
Few-Shot Image Classification and Segmentation as Visual Question Answering Using Vision-Language Models—0
Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated ExpertsCode1
BECLR: Batch Enhanced Contrastive Few-Shot LearningCode1
Few and Fewer: Learning Better from Few Examples Using Fewer Base ClassesCode0
LDCA: Local Descriptors with Contextual Augmentation for Few-Shot Learning—0
Transductive Zero-Shot and Few-Shot CLIPCode1
RAFIC: Retrieval-Augmented Few-shot Image ClassificationCode0
TALDS-Net: Task-Aware Adaptive Local Descriptors Selection for Few-shot Image Classification—0
Large Language Models are Good Prompt Learners for Low-Shot Image ClassificationCode1
Diversified in-domain synthesis with efficient fine-tuning for few-shot classificationCode1
Simple Semantic-Aided Few-Shot LearningCode1
Few-Shot Classification & Segmentation Using Large Language Models Agent—0
Are LSTMs Good Few-Shot Learners?Code0
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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+BPA (transductive)Accuracy85.59—Unverified
6PT+MAP+SF+SOT (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+SOT (transductive)Accuracy91.34—Unverified
9PT+MAP+SF+BPA (transductive)Accuracy91.34—Unverified
10AmdimNetAccuracy90.98—Unverified