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 26–50 of 353 papers

TitleStatusHype
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