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 201–225 of 305 papers

TitleStatusHype
Adaptive Image Transformer for One-Shot Object Detection—0
Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition—0
Adaptive Noise Resilient Keyword Spotting Using One-Shot Learning—0
Meta-Reinforcement Learning with Self-Modifying Networks—0
AFAT: Adaptive Failure-Aware Tracker for Robust Visual Object Tracking—0
AHAM: Adapt, Help, Ask, Model -- Harvesting LLMs for literature mining—0
A Hippocampus Model for Online One-Shot Storage of Pattern Sequences—0
A Model of Zero-Shot Learning of Spoken Language Understanding—0
An Event based Prediction Suffix Tree—0
An Exploration of Three Lightly-supervised Representation Learning Approaches for Named Entity Classification—0
Annotation-Free and One-Shot Learning for Instance Segmentation of Homogeneous Object Clusters—0
A One-Shot Learning Framework for Assessment of Fibrillar Collagen from Second Harmonic Generation Images of an Infarcted Myocardium—0
Application of Computer Vision Techniques for Segregation of PlasticWaste based on Resin Identification Code—0
AROS: Affordance Recognition with One-Shot Human Stances—0
ARTiS: Appearance-based Action Recognition in Task Space for Real-Time Human-Robot Collaboration—0
Assessing Shape Bias Property of Convolutional Neural Networks—0
A System For Robot Concept Learning Through Situated Dialogue—0
Attention-Based Multi-Context Guiding for Few-Shot Semantic Segmentation—0
Augmented Memory Networks for Streaming-Based Active One-Shot Learning—0
Augmented Memory Networks for Streaming-Based Active One-Shot Learning—0
A Unified approach for Conventional Zero-shot, Generalized Zero-shot and Few-shot Learning—0
Automatic detection of rare pathologies in fundus photographs using few-shot learning—0
AutoTinyBERT: Automatic Hyper-parameter Optimization for Efficient Pre-trained Language Models—0
Better Together: Resnet-50 accuracy with 13 fewer parameters and at 3 speed—0
Bias Testing and Mitigation in LLM-based Code Generation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified