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 151–175 of 305 papers

TitleStatusHype
Unsupervised Noisy Tracklet Person Re-identification—0
Visual Imitation with Reinforcement Learning using Recurrent Siamese Networks—0
Better Together: Resnet-50 accuracy with 13 fewer parameters and at 3 speed—0
One-Shot Learning with Triplet Loss for Vegetation Classification Tasks—0
Robust One Shot Audio to Video Generation—0
Contour Transformer Network for One-shot Segmentation of Anatomical StructuresCode0
Depression Status Estimation by Deep Learning based Hybrid Multi-Modal Fusion Model—0
ScaleNAS: One-Shot Learning of Scale-Aware Representations for Visual Recognition—0
Application of Computer Vision Techniques for Segregation of PlasticWaste based on Resin Identification Code—0
Resource Constrained Dialog Policy Learning via Differentiable Inductive Logic Programming—0
One-Shot Federated Learning with Neuromorphic Processors—0
Unsupervised One-shot Learning of Both Specific Instances and Generalised Classes with a Hippocampal ArchitectureCode0
One-shot Learning for Temporal Knowledge Graphs—0
Learning from similarity and information extraction from structured documentsCode0
Contour Primitive of Interest Extraction Network Based on One-Shot Learning for Object-Agnostic Vision Measurement—0
One-Shot learning based classification for segregation of plastic waste—0
Distilled One-Shot Federated Learning—0
DeepWriteSYN: On-Line Handwriting Synthesis via Deep Short-Term Representations—0
Exploiting Temporal Coherence for Self-Supervised One-shot Video Re-identification—0
Sorted Pooling in Convolutional Networks for One-shot Learning—0
Fast Video Object Segmentation With Temporal Aggregation Network and Dynamic Template Matching—0
Learning to Segment Anatomical Structures Accurately from One Exemplar—0
Leveraging Siamese Networks for One-Shot Intrusion Detection Model—0
AFAT: Adaptive Failure-Aware Tracker for Robust Visual Object Tracking—0
Physics-based polynomial neural networks for one-shot learning of dynamical systems from one or a few samples—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified