RankMixup: Ranking-Based Mixup Training for Network Calibration

no code implementations • ICCV 2023 • Jongyoun Noh, Hyekang Park, Junghyup Lee, Bumsub Ham

In this paper, we present RankMixup, a novel mixup-based framework alleviating the problem of the mixture of labels for network calibration.

ACLS: Adaptive and Conditional Label Smoothing for Network Calibration

no code implementations • ICCV 2023 • Hyekang Park, Jongyoun Noh, Youngmin Oh, Donghyeon Baek, Bumsub Ham

We present in this paper an in-depth analysis of existing regularization-based methods, providing a better understanding on how they affect to network calibration.

Image Classification Semantic Segmentation

Bi-directional Contrastive Learning for Domain Adaptive Semantic Segmentation

no code implementations • 22 Jul 2022 • Geon Lee, Chanho Eom, Wonkyung Lee, Hyekang Park, Bumsub Ham

We present a novel unsupervised domain adaptation method for semantic segmentation that generalizes a model trained with source images and corresponding ground-truth labels to a target domain.

Contrastive Learning Object +2