11 citations · 16 across the 8 of their papers we have counts for
9 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…
OnlineBEV: Recurrent Temporal Fusion in Bird's Eye View Representations for Multi-Camera 3D Perception
Junho Koh, Youngwoo Lee, Jungho Kim +2
Multi-view camera-based 3D perception can be conducted using bird's eye view (BEV) features obtained through perspective view-to-BEV transformations. Several studies have shown tha…
Fine-Grained Pillar Feature Encoding Via Spatio-Temporal Virtual Grid for 3D Object Detection
Konyul Park, Yecheol Kim, Junho Koh +2
Developing high-performance, real-time architectures for LiDAR-based 3D object detectors is essential for the successful commercialization of autonomous vehicles. Pillar-based meth…
Unified Contrastive Fusion Transformer for Multimodal Human Action Recognition
Kyoung Ok Yang, Junho Koh, Jun Won Choi
Various types of sensors have been considered to develop human action recognition (HAR) models. Robust HAR performance can be achieved by fusing multimodal data acquired by differe…
MGTANet: Encoding Sequential LiDAR Points Using Long Short-Term Motion-Guided Temporal Attention for 3D Object Detection
Junho Koh, Junhyung Lee, Youngwoo Lee +2
Most scanning LiDAR sensors generate a sequence of point clouds in real-time. While conventional 3D object detectors use a set of unordered LiDAR points acquired over a fixed time…
D-Align: Dual Query Co-attention Network for 3D Object Detection Based on Multi-frame Point Cloud Sequence
Junhyung Lee, Junho Koh, Youngwoo Lee +1
LiDAR sensors are widely used for 3D object detection in various mobile robotics applications. LiDAR sensors continuously generate point cloud data in real-time. Conventional 3D ob…
Joint 3D Object Detection and Tracking Using Spatio-Temporal Representation of Camera Image and LiDAR Point Clouds
Junho Koh, Jaekyum Kim, Jinhyuk Yoo +3
In this paper, we propose a new joint object detection and tracking (JoDT) framework for 3D object detection and tracking based on camera and LiDAR sensors. The proposed method, re…
Joint Representation of Temporal Image Sequences and Object Motion for Video Object Detection
Junho Koh, Jaekyum Kim, Younji Shin +3
In this paper, we propose a new video object detector (VoD) method referred to as temporal feature aggregation and motion-aware VoD (TM-VoD), which produces a joint representation…
Robust Deep Multi-modal Learning Based on Gated Information Fusion Network
Jaekyum Kim, Junho Koh, Yecheol Kim +3
The goal of multi-modal learning is to use complimentary information on the relevant task provided by the multiple modalities to achieve reliable and robust performance. Recently,…