47 citations · 52 across the 6 of their papers we have counts for
6 papers
Learning Probabilistic Prompt for Continual Learning
Hyekang Park, Sanghoon Lee, Geon Lee +2
Continual learning aims to progressively learn from a sequence of tasks, each containing a disjoint subset of classes, while preserving previously learned knowledge. Prompt-based c…
3DPillars: Pillar-based two-stage 3D object detection
Jongyoun Noh, Junghyup Lee, Hyekang Park +1
PointPillars is the fastest 3D object detector that exploits pseudo image representations to encode features for 3D objects in a scene. Albeit efficient, PointPillars is typically…
ACLS: Adaptive and Conditional Label Smoothing for Network Calibration
Hyekang Park, Jongyoun Noh, Youngmin Oh +2
We address the problem of network calibration adjusting miscalibrated confidences of deep neural networks. Many approaches to network calibration adopt a regularization-based metho…
RankMixup: Ranking-Based Mixup Training for Network Calibration
Jongyoun Noh, Hyekang Park, Junghyup Lee +1
Network calibration aims to accurately estimate the level of confidences, which is particularly important for employing deep neural networks in real-world systems. Recent approache…
HVPR: Hybrid Voxel-Point Representation for Single-stage 3D Object Detection
Jongyoun Noh, Sanghoon Lee, Bumsub Ham
We address the problem of 3D object detection, that is, estimating 3D object bounding boxes from point clouds. 3D object detection methods exploit either voxel-based or point-based…
Learning Memory-guided Normality for Anomaly Detection
Hyunjong Park, Jongyoun Noh, Bumsub Ham
We address the problem of anomaly detection, that is, detecting anomalous events in a video sequence. Anomaly detection methods based on convolutional neural networks (CNNs) typica…