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Resolving Class Imbalance for LiDAR-based Object Detector by Dynamic Weight Average and Contextual Ground Truth Sampling
Daeun Lee
*
,
Jinkyu Kim
*
Corresponding author for this work
Research output
:
Chapter in Book/Report/Conference proceeding
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Conference contribution
11
Citations (Scopus)
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Dive into the research topics of 'Resolving Class Imbalance for LiDAR-based Object Detector by Dynamic Weight Average and Contextual Ground Truth Sampling'. Together they form a unique fingerprint.
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Keyphrases
LiDAR
100%
Object Detector
100%
Class Imbalance
100%
Dynamic Weight Average
100%
Resolving Class
100%
3D Objects
66%
Data Imbalance Problem
66%
Detection Accuracy
33%
Imbalanced Data
33%
Suboptimal Performance
33%
Main Components
33%
Sampling Methods
33%
Point Cloud
33%
Semantic Information
33%
Multiplex Detection
33%
Autonomous Driving System
33%
Driving Dataset
33%
Method Effectiveness
33%
Real-world Driving
33%
KITTI Dataset
33%
Detection Head
33%
NuScenes Dataset
33%
Computer Science
Object Detector
100%
Class Imbalance
100%
Detection Accuracy
33%
Optimal Performance
33%
Point Cloud
33%
Sampling Technique
33%
Main Component
33%
Autonomous Driving
33%