2 code implementations • 8 Apr 2024 • Jaewoo Jeong, Daehee Park, Kuk-Jin Yoon
Our model effectively handles the multi-modality of human motion and the complexity of long-term multi-agent interactions, improving performance in complex environments.
1 code implementation • 15 Mar 2024 • Daehee Park, Jaeseok Jeong, Sung-Hoon Yoon, Jaewoo Jeong, Kuk-Jin Yoon
Our method surpasses the performance of existing state-of-the-art online learning methods in terms of both prediction accuracy and computational efficiency.
1 code implementation • 26 Dec 2023 • Daehee Park, Jaewoo Jeong, Kuk-Jin Yoon
To address this limitation, we propose a method based on continuous and stochastic representations of Neural Stochastic Differential Equations (NSDE) for alleviating discrepancies due to data acquisition strategy.
no code implementations • 24 May 2023 • Daehee Park, Hobin Ryu, Yunseo Yang, Jegyeong Cho, Jiwon Kim, Kuk-Jin Yoon
We also model the interaction using a probabilistic distribution, which allows for multiple possible future interactions.
Ranked #2 on Trajectory Prediction on nuScenes
no code implementations • 12 Dec 2021 • Changgyoon Oh, Wonjune Cho, Daehee Park, Yujeong Chae, Lin Wang, Kuk-Jin Yoon
Providing omnidirectional depth along with RGB information is important for numerous applications, eg, VR/AR.
1 code implementation • ICCV 2021 • Hyeokjun Kweon, Sung-Hoon Yoon, Hyeonseong Kim, Daehee Park, Kuk-Jin Yoon
In this paper, we review the potential of the pre-trained classifier which is trained on the raw images.
Ranked #30 on Weakly-Supervised Semantic Segmentation on COCO 2014 val
Weakly supervised Semantic Segmentation Weakly-Supervised Semantic Segmentation