no code implementations • 24 Mar 2024 • JunBo Wang, Wenhai Liu, Qiaojun Yu, Yang You, Liu Liu, Weiming Wang, Cewu Lu
Our primary contribution is a Robust Articulation Network (RoArtNet) that is able to predict both joint parameters and affordable points robustly by local feature learning and point tuple voting.
no code implementations • 20 Mar 2024 • Qiaojun Yu, Ce Hao, JunBo Wang, Wenhai Liu, Liu Liu, Yao Mu, Yang You, Hengxu Yan, Cewu Lu
Robotic manipulation in everyday scenarios, especially in unstructured environments, requires skills in pose-aware object manipulation (POM), which adapts robots' grasping and handling according to an object's 6D pose.
no code implementations • 24 Oct 2023 • Chengzhi Yao, Zhi Li, JunBo Wang
To tackle the above issues, we focus on the essence of traffic system and propose STHODE: Spatio-Temporal Hypergraph Neural Ordinary Differential Equation Network, which combines road network topology and traffic dynamics to capture high-order spatio-temporal dependencies in traffic data.
1 code implementation • 28 Sep 2023 • Qiaojun Yu, JunBo Wang, Wenhai Liu, Ce Hao, Liu Liu, Lin Shao, Weiming Wang, Cewu Lu
Results show that GAMMA significantly outperforms SOTA articulation modeling and manipulation algorithms in unseen and cross-category articulated objects.
no code implementations • 2 Jul 2023 • Hao-Shu Fang, Hongjie Fang, Zhenyu Tang, Jirong Liu, Chenxi Wang, JunBo Wang, Haoyi Zhu, Cewu Lu
A key challenge in robotic manipulation in open domains is how to acquire diverse and generalizable skills for robots.
no code implementations • 16 Mar 2021 • Hongjie He, Ke Yang, Yuwei Cai, Zijian Jiang, Qiutong Yu, Kun Zhao, JunBo Wang, Sarah Narges Fatholahi, Yan Liu, Hasti Andon Petrosians, Bingxu Hu, Liyuan Qing, Zhehan Zhang, Hongzhang Xu, Siyu Li, Kyle Gao, Linlin Xu, Jonathan Li
Building rooftop data are of importance in several urban applications and in natural disaster management.
no code implementations • CVPR 2018 • Junbo Wang, Wei Wang, Yan Huang, Liang Wang, Tieniu Tan
Inspired by the facts that memory modelling poses potential advantages to long-term sequential problems [35] and working memory is the key factor of visual attention [33], we propose a Multimodal Memory Model (M3) to describe videos, which builds a visual and textual shared memory to model the long-term visual-textual dependency and further guide visual attention on described visual targets to solve visual-textual alignments.
no code implementations • 17 Nov 2016 • Junbo Wang, Wei Wang, Yan Huang, Liang Wang, Tieniu Tan
In this paper, we propose a Multimodal Memory Model (M3) to describe videos, which builds a visual and textual shared memory to model the long-term visual-textual dependency and further guide global visual attention on described targets.