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Jee-Hyong Lee*

2 accepted papers

2024

ExMatch: Self-guided Exploitation for Semi-Supervised Learning with Scarce Labeled Samples

ECCV 2024poster

"Semi-supervised learning is a learning method that uses both labeled and unlabeled samples to improve the performance of the model while reducing labeling costs. When there were tens to hundreds of labeled samples, semi-supervised learning methods showed good performance, but most of them showed po…

Cited by 0SourcePDFScholar
2024

IGNORE: Information Gap-based False Negative Loss Rejection for Single Positive Multi-Label Learning

ECCV 2024poster

"Single Positive Multi-Label Learning (SPML) is a method for a scarcely annotated setting, in which each image is assigned only one positive label while the other labels remain unannotated. Most approaches for SPML assume unannotated labels as negatives (“Assumed Negative”, AN). However, with this a…

Cited by 1SourcePDFScholar