no code implementations • 4 Apr 2024 • Lei Zhou, Haozhe Wang, Zhengshen Zhang, Zhiyang Liu, Francis EH Tay, adn Marcelo H. Ang. Jr
In the realm of robotic grasping, achieving accurate and reliable interactions with the environment is a pivotal challenge.
1 code implementation • 5 Sep 2023 • Lei Zhou, Zhiyang Liu, Runze Gan, Haozhe Wang, Marcelo H. Ang Jr
In the second stage, a novel registration network is designed to extract pose-sensitive features and predict the representation of object partial point cloud in canonical space based on the deformation results from the first stage.
no code implementations • ICCV 2023 • Yu Wu, Yana Wei, Haozhe Wang, Yongfei Liu, Sibei Yang, Xuming He
This paper introduces Grounded Image Text Matching with Mismatched Relation (GITM-MR), a novel visual-linguistic joint task that evaluates the relation understanding capabilities of transformer-based pre-trained models.
1 code implementation • 14 Jul 2023 • Zhili Ng, Haozhe Wang, Zhengshen Zhang, Francis Tay Eng Hock, Marcelo H. Ang Jr
In this work, we present SynTable, a unified and flexible Python-based dataset generator built using NVIDIA's Isaac Sim Replicator Composer for generating high-quality synthetic datasets for unseen object amodal instance segmentation of cluttered tabletop scenes.
no code implementations • 12 Jun 2023 • Haozhe Wang, Chao Du, Panyan Fang, Li He, Liang Wang, Bo Zheng
In this regard, we explore the problem of constrained bidding in adversarial bidding environments, which assumes no knowledge about the adversarial factors.
1 code implementation • 10 Jun 2022 • Haozhe Wang, Chao Du, Panyan Fang, Shuo Yuan, Xuming He, Liang Wang, Bo Zheng
Real-Time Bidding (RTB) is an important mechanism in modern online advertising systems.
1 code implementation • 30 Mar 2022 • Haozhe Wang, Zhiyang Liu, Lei Zhou, Huan Yin, Marcelo H Ang Jr
Vision-based grasp estimation is an essential part of robotic manipulation tasks in the real world.
no code implementations • 3 Mar 2020 • Haozhe Wang, Jiale Zhou, Xuming He
Despite recent success of deep network-based Reinforcement Learning (RL), it remains elusive to achieve human-level efficiency in learning novel tasks.