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

HVPR: Hybrid Voxel-Point Representation for Single-stage 3D Object Detection

1 code implementation • CVPR 2021 • Jongyoun Noh, SangHoon Lee, Bumsub Ham

To this end, we propose a new convolutional neural network (CNN) architecture, dubbed HVPR, that integrates both features into a single 3D representation effectively and efficiently.

3D Object Detection Object +1

Learning Memory-guided Normality for Anomaly Detection

2 code implementations • CVPR 2020 • Hyunjong Park, Jongyoun Noh, Bumsub Ham

To address this problem, we present an unsupervised learning approach to anomaly detection that considers the diversity of normal patterns explicitly, while lessening the representation capacity of CNNs.

Anomaly Detection In Surveillance Videos General Action Video Anomaly Detection +1