1 code implementation • 1 Dec 2022 • Junho Koh, Junhyung Lee, Youngwoo Lee, Jaekyum Kim, Jun Won Choi
While conventional 3D object detectors use a set of unordered LiDAR points acquired over a fixed time interval, recent studies have revealed that substantial performance improvement can be achieved by exploiting the spatio-temporal context present in a sequence of LiDAR point sets.
Ranked #25 on 3D Object Detection on nuScenes
no code implementations • 14 Dec 2021 • Junho Koh, Jaekyum Kim, Jinhyuk Yoo, Yecheol Kim, Jun Won Choi
The detector constructs the spatio-temporal features via the weighted temporal aggregation of the spatial features obtained by the camera and LiDAR fusion.
1 code implementation • 20 Nov 2020 • Junho Koh, Jaekyum Kim, Younji Shin, Byeongwon Lee, Seungji Yang, Jun Won Choi
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 of temporal image sequences and object motion.
1 code implementation • International Conference on Computer Vision Workshops 2019 • Dawei Du, Pengfei Zhu, Longyin Wen, Xiao Bian, Haibin Lin, QinGhua Hu, Tao Peng, Jiayu Zheng, Xinyao Wang, Yue Zhang, Liefeng Bo, Hailin Shi, Rui Zhu, Aashish Kumar, Aijin Li, Almaz Zinollayev, Anuar Askergaliyev, Arne Schumann, Binjie Mao, Byeongwon Lee, Chang Liu, Changrui Chen, Chunhong Pan, Chunlei Huo, Da Yu, Dechun Cong, Dening Zeng, Dheeraj Reddy Pailla, Di Li, Dong Wang, Donghyeon Cho, Dongyu Zhang, Furui Bai, George Jose, Guangyu Gao, Guizhong Liu, Haitao Xiong, Hao Qi, Haoran Wang, Heqian Qiu, Hongliang Li, Huchuan Lu, Ildoo Kim, Jaekyum Kim, Jane Shen, Jihoon Lee, Jing Ge, Jingjing Xu, Jingkai Zhou, Jonas Meier, Jun Won Choi, Junhao Hu, Junyi Zhang, Junying Huang, Kaiqi Huang, Keyang Wang, Lars Sommer, Lei Jin, Lei Zhang
Results of 33 object detection algorithms are presented.
no code implementations • 17 Jul 2018 • Jaekyum Kim, Junho Koh, Yecheol Kim, Jaehyung Choi, Youngbae Hwang, Jun Won Choi
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.
no code implementations • 24 Apr 2017 • ByeoungDo Kim, Chang Mook Kang, Seung Hi Lee, Hyunmin Chae, Jaekyum Kim, Chung Choo Chung, Jun Won Choi
Our approach is data-driven and simple to use in that it learns complex behavior of the vehicles from the massive amount of trajectory data through deep neural network model.
2 code implementations • 8 Feb 2017 • Hyunmin Chae, Chang Mook Kang, ByeoungDo Kim, Jaekyum Kim, Chung Choo Chung, Jun Won Choi
In this paper, we propose a new autonomous braking system based on deep reinforcement learning.