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

Showing 151175 of 353 papers

TitleStatusHype
Improved Few-Shot Image Classification Through Multiple-Choice Questions0
Assessing Sample Quality via the Latent Space of Generative ModelsCode0
Siamese Transformer Networks for Few-shot Image Classification0
Improving Hyperbolic Representations via Gromov-Wasserstein Regularization0
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 Classification0
Variational Neuron Shifting for Few-Shot Image Classification Across Domains0
Frozen Feature Augmentation for Few-Shot Image Classification0
Few-Shot Image Classification and Segmentation as Visual Question Answering Using Vision-Language Models0
Few and Fewer: Learning Better from Few Examples Using Fewer Base ClassesCode0
LDCA: Local Descriptors with Contextual Augmentation for Few-Shot Learning0
RAFIC: Retrieval-Augmented Few-shot Image ClassificationCode0
TALDS-Net: Task-Aware Adaptive Local Descriptors Selection for Few-shot Image Classification0
Few-Shot Classification & Segmentation Using Large Language Models Agent0
Are LSTMs Good Few-Shot Learners?Code0
Subspace Adaptation Prior for Few-Shot LearningCode0
PrototypeFormer: Learning to Explore Prototype Relationships for Few-shot Image Classification0
Logarithm-transform aided Gaussian Sampling for Few-Shot LearningCode0
MVP: Meta Visual Prompt Tuning for Few-Shot Remote Sensing Image Scene Classification0
PRE: Vision-Language Prompt Learning with Reparameterization EncoderCode0
Support-Set Context Matters for Bongard ProblemsCode0
Knowledge-Aware Prompt Tuning for Generalizable Vision-Language Models0
Few-shot Image Classification based on Gradual Machine Learning0
DiffKendall: A Novel Approach for Few-Shot Learning with Differentiable Kendall's Rank CorrelationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SgVA-CLIPAccuracy97.95Unverified
2CAML [Laion-2b]Accuracy96.2Unverified
3P>M>F (P=DINO-ViT-base, M=ProtoNet)Accuracy95.3Unverified
4TRIDENTAccuracy86.11Unverified
5PT+MAP+SF+BPA (transductive)Accuracy85.59Unverified
6PT+MAP+SF+SOT (transductive)Accuracy85.59Unverified
7PEMnE-BMS* (transductive)Accuracy85.54Unverified
8PT+MAP (s+f) (transductive)Accuracy84.81Unverified
9BAVARDAGEAccuracy84.8Unverified
10EASY 3xResNet12 (transductive)Accuracy84.04Unverified
#ModelMetricClaimedVerifiedStatus
1SgVA-CLIPAccuracy98.72Unverified
2CAML [Laion-2b]Accuracy98.6Unverified
3P>M>F (P=DINO-ViT-base, M=ProtoNet)Accuracy98.4Unverified
4TRIDENTAccuracy95.95Unverified
5BAVARDAGEAccuracy91.65Unverified
6PEMnE-BMS*(transductive)Accuracy91.53Unverified
7Transductive CNAPS + FETIAccuracy91.5Unverified
8PT+MAP+SF+SOT (transductive)Accuracy91.34Unverified
9PT+MAP+SF+BPA (transductive)Accuracy91.34Unverified
10AmdimNetAccuracy90.98Unverified