1 citations · 2 across the 10 of their papers we have counts for
12 papers
Horizon3D: Sparse Radar-Camera Fusion for Long-Range 3D Perception in Autonomous Driving
Geonho Bang, Geunju Baek, Dongyoung Lee +2
Long-range 3D object detection is critical for safe autonomous driving at highway speeds, yet existing radar-camera fusion methods remain limited at extended ranges. BEV-based meth…
Mining Instance-Centric Vision-Language Contexts for Human-Object Interaction Detection
Soo Won Seo, KyungChae Lee, Hyungchan Cho +3
Human-Object Interaction (HOI) detection aims to localize human-object pairs and classify their interactions from a single image, a task that demands strong visual understanding an…
SafeDrive: Fine-Grained Safety Reasoning for End-to-End Driving in a Sparse World
Jungho Kim, Jiyong Oh, Seunghoon Yu +3
The end-to-end (E2E) paradigm, which maps sensor inputs directly to driving decisions, has recently attracted significant attention due to its unified modeling capability and scala…
Habilis-: A Fast-Motion and Long-Lasting On-Device Vision-Language-Action Model
Tommoro Robotics, :, Jesoon Kang +22
We introduce Habilis-, a fast-motion and long-lasting on-device vision-language-action (VLA) model designed for real-world deployment. Current VLA evaluation remains largely con…
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…
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…
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,…
JARViS: Detecting Actions in Video Using Unified Actor-Scene Context Relation Modeling
Seok Hwan Lee, Taein Son, Soo Won Seo +2
Video action detection (VAD) is a formidable vision task that involves the localization and classification of actions within the spatial and temporal dimensions of a video clip. Am…
PillarGen: Enhancing Radar Point Cloud Density and Quality via Pillar-based Point Generation Network
Jisong Kim, Geonho Bang, Kwangjin Choi +4
In this paper, we present a novel point generation model, referred to as Pillar-based Point Generation Network (PillarGen), which facilitates the transformation of point clouds fro…
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…