7 papers
RCTDistill: Cross-Modal Knowledge Distillation Framework for Radar-Camera 3D Object Detection with Temporal Fusion
Geonho Bang, Minjae Seong, Jisong Kim +5
Radar-camera fusion methods have emerged as a cost-effective approach for 3D object detection but still lag behind LiDAR-based methods in performance. Recent works have focused on…
MAESTRO: Task-Relevant Optimization via Adaptive Feature Enhancement and Suppression for Multi-task 3D Perception
Changwon Kang, Jisong Kim, Hongjae Shin +2
The goal of multi-task learning is to learn to conduct multiple tasks simultaneously based on a shared data representation. While this approach can improve learning efficiency, it…
RadarDistill: Boosting Radar-based Object Detection Performance via Knowledge Distillation from LiDAR Features
Geonho Bang, Kwangjin Choi, Jisong Kim +2
The inherent noisy and sparse characteristics of radar data pose challenges in finding effective representations for 3D object detection. In this paper, we propose RadarDistill, a…
JoVALE: Detecting Human Actions in Video Using Audiovisual and Language Contexts
Taein Son, Soo Won Seo, Jisong Kim +2
Video Action Detection (VAD) entails localizing and categorizing action instances within videos, which inherently consist of diverse information sources such as audio, visual cues,…
MR-Occ: Efficient Camera-LiDAR 3D Semantic Occupancy Prediction Using Hierarchical Multi-Resolution Voxel Representation
Minjae Seong, Jisong Kim, Geonho Bang +2
Accurate 3D perception is essential for understanding the environment in autonomous driving. Recent advancements in 3D semantic occupancy prediction have leveraged camera-LiDAR fus…
CRT-Fusion: Camera, Radar, Temporal Fusion Using Motion Information for 3D Object Detection
Jisong Kim, Minjae Seong, Jun Won Choi
Accurate and robust 3D object detection is a critical component in autonomous vehicles and robotics. While recent radar-camera fusion methods have made significant progress by fusi…