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
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
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
Few-Shot Image Classification and Segmentation as Visual Question Answering Using Vision-Language Models—0
Frozen Feature Augmentation for Few-Shot Image Classification—0
Few and Fewer: Learning Better from Few Examples Using Fewer Base ClassesCode0
LDCA: Local Descriptors with Contextual Augmentation for Few-Shot Learning—0
RAFIC: Retrieval-Augmented Few-shot Image ClassificationCode0
TALDS-Net: Task-Aware Adaptive Local Descriptors Selection for Few-shot Image Classification—0
Few-Shot Classification & Segmentation Using Large Language Models Agent—0
Are LSTMs Good Few-Shot Learners?Code0
Subspace Adaptation Prior for Few-Shot LearningCode0
PrototypeFormer: Learning to Explore Prototype Relationships for Few-shot Image Classification—0
Logarithm-transform aided Gaussian Sampling for Few-Shot LearningCode0
MVP: Meta Visual Prompt Tuning for Few-Shot Remote Sensing Image Scene Classification—0
PRE: Vision-Language Prompt Learning with Reparameterization EncoderCode0
Support-Set Context Matters for Bongard ProblemsCode0
Knowledge-Aware Prompt Tuning for Generalizable Vision-Language Models—0
Few-shot Image Classification based on Gradual Machine Learning—0
DiffKendall: A Novel Approach for Few-Shot Learning with Differentiable Kendall's Rank CorrelationCode0
Feature Activation Map: Visual Explanation of Deep Learning Models for Image Classification—0
FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?—0
Distilling Self-Supervised Vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentation—0
Multi-Similarity Contrastive Learning—0
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy ImitationCode0
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
Strong Baselines for Parameter Efficient Few-Shot Fine-tuning—0
Boosting Few-Shot Text Classification via Distribution Estimation—0
RotoGBML: Towards Out-of-Distribution Generalization for Gradient-Based Meta-Learning—0
Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning—0
Small Sample Hyperspectral Image Classification Based on the Random Patches Network and Recursive FilteringCode0
CMVAE: Causal Meta VAE for Unsupervised Meta-LearningCode0
CovidExpert: A Triplet Siamese Neural Network framework for the detection of COVID-19—0
On the Efficacy of Differentially Private Few-shot Image ClassificationCode0
Explore the Power of Dropout on Few-shot Learning—0
ProD: Prompting-To-Disentangle Domain Knowledge for Cross-Domain Few-Shot Image Classification—0
Exploiting Category Names for Few-Shot Classification with Vision-Language Models—0
SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for Few-shot Image ClassificationCode0
KGTN-ens: Few-Shot Image Classification with Knowledge Graph EnsemblesCode0
Visual-Semantic Contrastive Alignment for Few-Shot Image Classification—0
Few-Shot Learning of Compact Models via Task-Specific Meta Distillation—0
MergedNET: A simple approach for one-shot learning in siamese networks based on similarity layersCode0
A Unified Framework with Meta-dropout for Few-shot Learning—0
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification—0
PaLI: A Jointly-Scaled Multilingual Language-Image ModelCode0
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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