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 21–30 of 305 papers

TitleStatusHype
One-Shot Learning Meets Depth Diffusion in Multi-Object Videos—0
Emulating Brain-like Rapid Learning in Neuromorphic Edge ComputingCode0
CrossFi: A Cross Domain Wi-Fi Sensing Framework Based on Siamese NetworkCode1
Correlation Weighted Prototype-based Self-Supervised One-Shot Segmentation of Medical Images—0
MU-MAE: Multimodal Masked Autoencoders-Based One-Shot Learning—0
One-Shot Collaborative Data DistillationCode0
Encoding Matching Criteria for Cross-domain Deformable Image RegistrationCode0
Object Detection using Oriented Window Learning Vi-sion Transformer: Roadway Assets Recognition—0
Latent Diffusion Model-Enabled Low-Latency Semantic Communication in the Presence of Semantic Ambiguities and Wireless Channel NoisesCode1
Npix2Cpix: A GAN-Based Image-to-Image Translation Network With Retrieval- Classification Integration for Watermark Retrieval From Historical Document Images—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified