1 code implementation • 2 Dec 2024 • Zewei Zhou, Hao Xiang, Zhaoliang Zheng, Seth Z. Zhao, Mingyue Lei, Yun Zhang, Tianhui Cai, Xinyi Liu, Johnson Liu, Maheswari Bajji, Jacob Pham, Xin Xia, Zhiyu Huang, Bolei Zhou, Jiaqi Ma
Vehicle-to-everything (V2X) technologies offer a promising paradigm to mitigate the limitations of constrained observability in single-vehicle systems.
1 code implementation • 22 Oct 2024 • Hao Xiang, Bowen Yu, Hongyu Lin, Keming Lu, Yaojie Lu, Xianpei Han, Le Sun, Jingren Zhou, Junyang Lin
The key to automated alignment lies in providing learnable and accurate preference signals for preference learning without human annotation.
no code implementations • 20 Aug 2024 • Seth Z. Zhao, Hao Xiang, Chenfeng Xu, Xin Xia, Bolei Zhou, Jiaqi Ma
Existing Vehicle-to-Everything (V2X) cooperative perception methods rely on accurate multi-agent 3D annotations.
1 code implementation • 3 Jun 2024 • Boxi Cao, Keming Lu, Xinyu Lu, Jiawei Chen, Mengjie Ren, Hao Xiang, Peilin Liu, Yaojie Lu, Ben He, Xianpei Han, Le Sun, Hongyu Lin, Bowen Yu
Alignment is the most critical step in building large language models (LLMs) that meet human needs.
no code implementations • 24 Mar 2024 • Hao Xiang, Zhaoliang Zheng, Xin Xia, Runsheng Xu, Letian Gao, Zewei Zhou, Xu Han, Xinkai Ji, Mingxi Li, Zonglin Meng, Li Jin, Mingyue Lei, Zhaoyang Ma, Zihang He, Haoxuan Ma, Yunshuang Yuan, Yingqian Zhao, Jiaqi Ma
Recent advancements in Vehicle-to-Everything (V2X) technologies have enabled autonomous vehicles to share sensing information to see through occlusions, greatly boosting the perception capability.
1 code implementation • 14 Mar 2024 • Zhuoqun Li, Hongyu Lin, Yaojie Lu, Hao Xiang, Xianpei Han, Le Sun
Declarative knowledge and procedural knowledge are two key parts in meta-cognitive theory, and these two hold significant importance in pre-training and inference of LLMs.
no code implementations • ICCV 2023 • Wentao Jiang, Hao Xiang, Xinyu Cai, Runsheng Xu, Jiaqi Ma, Yikang Li, Gim Hee Lee, Si Liu
We define perceptual gain as the increased perceptual capability when a new LiDAR is placed.
no code implementations • 31 Aug 2023 • Si Liu, Chen Gao, Yuan Chen, Xingyu Peng, Xianghao Kong, Kun Wang, Runsheng Xu, Wentao Jiang, Hao Xiang, Jiaqi Ma, Miao Wang
Specifically, we analyze the performance changes of different methods under different bandwidths, providing a deep insight into the performance-bandwidth trade-off issue.
no code implementations • 17 Jun 2023 • Qinru Li, Hao Xiang
Reinforcement Learning has achieved tremendous success in the many Atari games.
1 code implementation • ICCV 2023 • Hao Xiang, Runsheng Xu, Jiaqi Ma
We present HM-ViT, the first unified multi-agent hetero-modal cooperative perception framework that can collaboratively predict 3D objects for highly dynamic vehicle-to-vehicle (V2V) collaborations with varying numbers and types of agents.
2 code implementations • CVPR 2023 • Runsheng Xu, Xin Xia, Jinlong Li, Hanzhao Li, Shuo Zhang, Zhengzhong Tu, Zonglin Meng, Hao Xiang, Xiaoyu Dong, Rui Song, Hongkai Yu, Bolei Zhou, Jiaqi Ma
To facilitate the development of cooperative perception, we present V2V4Real, the first large-scale real-world multi-modal dataset for V2V perception.
1 code implementation • 11 Mar 2023 • Zhijie Xiao, Zhicheng Dong, Hao Xiang
In recent years, self-supervised learning has attracted widespread academic debate and addressed many of the key issues of computer vision.
1 code implementation • 27 Sep 2022 • Hao Xiang, Runsheng Xu, Xin Xia, Zhaoliang Zheng, Bolei Zhou, Jiaqi Ma
Recent advancements in Vehicle-to-Everything communication technology have enabled autonomous vehicles to share sensory information to obtain better perception performance.
2 code implementations • 5 Jul 2022 • Runsheng Xu, Zhengzhong Tu, Hao Xiang, Wei Shao, Bolei Zhou, Jiaqi Ma
The extensive experiments on the V2V perception dataset, OPV2V, demonstrate that CoBEVT achieves state-of-the-art performance for cooperative BEV semantic segmentation.
1 code implementation • 20 Mar 2022 • Runsheng Xu, Hao Xiang, Zhengzhong Tu, Xin Xia, Ming-Hsuan Yang, Jiaqi Ma
In this paper, we investigate the application of Vehicle-to-Everything (V2X) communication to improve the perception performance of autonomous vehicles.
Ranked #1 on
3D Object Detection
on V2XSet
2 code implementations • 16 Sep 2021 • Runsheng Xu, Hao Xiang, Xin Xia, Xu Han, Jinlong Li, Jiaqi Ma
We then construct a comprehensive benchmark with a total of 16 implemented models to evaluate several information fusion strategies~(i. e. early, late, and intermediate fusion) with state-of-the-art LiDAR detection algorithms.
Ranked #2 on
3D Object Detection
on OPV2V
no code implementations • 8 Jun 2020 • David Paz, Hengyuan Zhang, Qinru Li, Hao Xiang, Henrik Christensen
Recent advancements in statistical learning and computational abilities have enabled autonomous vehicle technology to develop at a much faster rate.