SOTAVerified

One-Shot Learning

One-shot learning is the task of learning information about object categories from a single training example.

( Image credit: Siamese Neural Networks for One-shot Image Recognition )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–75 of 305 papers

TitleStatusHype
Harmonization Across Imaging Locations(HAIL): One-Shot Learning for Brain MRI—0
One-shot lip-based biometric authentication: extending behavioral features with authentication phrase information—0
One-shot Joint Extraction, Registration and Segmentation of Neuroimaging DataCode0
An In-Depth Evaluation of Federated Learning on Biomedical Natural Language ProcessingCode1
One-Shot Learning for Periocular Recognition: Exploring the Effect of Domain Adaptation and Data Bias on Deep Representations—0
One-Shot Learning of Visual Path Navigation for Autonomous Vehicles—0
UOD: Universal One-shot Detection of Anatomical LandmarksCode1
Improving Knowledge Extraction from LLMs for Task Learning through Agent Analysis—0
One-shot Learning for Channel Estimation in Massive MIMO Systems—0
GPT Self-Supervision for a Better Data Annotator—0
One shot learning based drivers head movement identification using a millimetre wave radar sensor—0
Towards Consistent Video Editing with Text-to-Image Diffusion Models—0
Task Adaptive Feature Transformation for One-Shot Learning—0
CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and GeneralizationCode1
VGTS: Visually Guided Text Spotting for Novel Categories in Historical Manuscripts—0
A Novel Embedding Architecture and Score Level Fusion Scheme for Occluded Image Acquisition in Ear Biometrics SystemCode0
Self-Supervised One-Shot Learning for Automatic Segmentation of StyleGAN ImagesCode0
Tab2KG: Semantic Table Interpretation with Lightweight Semantic ProfilesCode0
PaCaNet: A Study on CycleGAN with Transfer Learning for Diversifying Fused Chinese Painting and Calligraphy—0
One-shot skill assessment in high-stakes domains with limited data via meta learningCode0
Population Template-Based Brain Graph Augmentation for Improving One-Shot Learning Classification—0
One-shot recognition of any material anywhere using contrastive learning with physics-based renderingCode0
Learning New Tasks from a Few Examples with Soft-Label PrototypesCode0
A few-shot learning approach with domain adaptation for personalized real-life stress detection in close relationshipsCode0
AROS: Affordance Recognition with One-Shot Human Stances—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified