1 code implementation • 11 Apr 2024 • Yikang Shen, Zhen Guo, Tianle Cai, Zengyi Qin
Large Language Models (LLMs) have achieved remarkable results, but their increasing resource demand has become a major obstacle to the development of powerful and accessible super-human intelligence.
1 code implementation • 3 Dec 2023 • Zengyi Qin, Wenliang Zhao, Xumin Yu, Xin Sun
The voice styles are not directly copied from and constrained by the style of the reference speaker.
1 code implementation • 6 Jan 2022 • Zengyi Qin, Dawei Sun, Chuchu Fan
Control certificates based on barrier functions have been a powerful tool to generate probably safe control policies for dynamical systems.
1 code implementation • 14 Sep 2021 • Yue Meng, Zengyi Qin, Chuchu Fan
Reactive and safe agent modelings are important for nowadays traffic simulator designs and safe planning applications.
no code implementations • 14 Sep 2021 • Charles Dawson, Zengyi Qin, Sicun Gao, Chuchu Fan
Safety and stability are common requirements for robotic control systems; however, designing safe, stable controllers remains difficult for nonlinear and uncertain models.
no code implementations • 24 Jun 2021 • Zengyi Qin, Yuxiao Chen, Chuchu Fan
We study constrained reinforcement learning (CRL) from a novel perspective by setting constraints directly on state density functions, rather than the value functions considered by previous works.
no code implementations • 18 Apr 2021 • Zengyi Qin, Jinglu Wang, Yan Lu
Detecting and localizing objects in the real 3D space, which plays a crucial role in scene understanding, is particularly challenging given only a monocular image due to the geometric information loss during imagery projection.
1 code implementation • ICLR 2021 • Zengyi Qin, Kaiqing Zhang, Yuxiao Chen, Jingkai Chen, Chuchu Fan
We propose a novel joint-learning framework that can be implemented in a decentralized fashion, with generalization guarantees for certain function classes.
1 code implementation • 28 Jul 2020 • Zengyi Qin, Jinglu Wang, Yan Lu
A crucial task in scene understanding is 3D object detection, which aims to detect and localize the 3D bounding boxes of objects belonging to specific classes.
no code implementations • 26 Oct 2019 • Zengyi Qin, Kuan Fang, Yuke Zhu, Li Fei-Fei, Silvio Savarese
For this purpose, we present KETO, a framework of learning keypoint representations of tool-based manipulation.
Robotics
1 code implementation • CVPR 2019 • Zengyi Qin, Jinglu Wang, Yan Lu
In this paper, we study the problem of 3D object detection from stereo images, in which the key challenge is how to effectively utilize stereo information.
1 code implementation • 26 Nov 2018 • Zengyi Qin, Jinglu Wang, Yan Lu
We propose MonoGRNet for the amodal 3D object detection from a monocular RGB image via geometric reasoning in both the observed 2D projection and the unobserved depth dimension.
Ranked #26 on Monocular 3D Object Detection on KITTI Cars Moderate