no code implementations • 4 Apr 2024 • Juanwu Lu, Can Cui, Yunsheng Ma, Aniket Bera, Ziran Wang
In this paper, we propose the Sequential Neural Variational Agent (SeNeVA), a generative model that describes the distribution of future trajectories for a single moving object.
no code implementations • 20 Feb 2024 • Wenqian Ye, Guangtao Zheng, Xu Cao, Yunsheng Ma, Xia Hu, Aidong Zhang
Machine learning systems are known to be sensitive to spurious correlations between biased features of the inputs (e. g., background, texture, and secondary objects) and the corresponding labels.
no code implementations • 14 Dec 2023 • Can Cui, Zichong Yang, Yupeng Zhou, Yunsheng Ma, Juanwu Lu, Lingxi Li, Yaobin Chen, Jitesh Panchal, Ziran Wang
Autonomous driving systems are increasingly popular in today's technological landscape, where vehicles with partial automation have already been widely available on the market, and the full automation era with "driverless" capabilities is near the horizon.
1 code implementation • 7 Dec 2023 • Yunsheng Ma, Can Cui, Xu Cao, Wenqian Ye, Peiran Liu, Juanwu Lu, Amr Abdelraouf, Rohit Gupta, Kyungtae Han, Aniket Bera, James M. Rehg, Ziran Wang
Autonomous driving (AD) has made significant strides in recent years.
1 code implementation • 21 Nov 2023 • Can Cui, Yunsheng Ma, Xu Cao, Wenqian Ye, Yang Zhou, Kaizhao Liang, Jintai Chen, Juanwu Lu, Zichong Yang, Kuei-Da Liao, Tianren Gao, Erlong Li, Kun Tang, Zhipeng Cao, Tong Zhou, Ao Liu, Xinrui Yan, Shuqi Mei, Jianguo Cao, Ziran Wang, Chao Zheng
We first introduce the background of Multimodal Large Language Models (MLLMs), the multimodal models development using LLMs, and the history of autonomous driving.
1 code implementation • 25 Oct 2023 • Yunsheng Ma, Juanwu Lu, Can Cui, Sicheng Zhao, Xu Cao, Wenqian Ye, Ziran Wang
We approach this objective by identifying the key challenges of shifting from single-agent to cooperative settings, adapting the model by freezing most of its parameters and adding a few lightweight modules.
no code implementations • 12 Oct 2023 • Can Cui, Yunsheng Ma, Xu Cao, Wenqian Ye, Ziran Wang
The fusion of human-centric design and artificial intelligence (AI) capabilities has opened up new possibilities for next-generation autonomous vehicles that go beyond transportation.
no code implementations • 19 Sep 2023 • Can Cui, Yunsheng Ma, Xu Cao, Wenqian Ye, Ziran Wang
The future of autonomous vehicles lies in the convergence of human-centric design and advanced AI capabilities.
1 code implementation • 12 Jun 2023 • Wenqian Ye, Yunsheng Ma, Xu Cao, Kun Tang
Though Transformers have achieved promising results in many computer vision tasks, they tend to be over-confident in predictions, as the standard Dot Product Self-Attention (DPSA) can barely preserve distance for the unbounded input domain.
1 code implementation • 27 May 2023 • Can Cui, Yunsheng Ma, Juanwu Lu, Ziran Wang
Sensor fusion is a crucial augmentation technique for improving the accuracy and reliability of perception systems for automated vehicles under diverse driving conditions.
no code implementations • 13 May 2023 • Yunsheng Ma, Wenqian Ye, Xu Cao, Amr Abdelraouf, Kyungtae Han, Rohit Gupta, Ziran Wang
Driver intention prediction seeks to anticipate drivers' actions by analyzing their behaviors with respect to surrounding traffic environments.
1 code implementation • 13 May 2023 • Yunsheng Ma, Liangqi Yuan, Amr Abdelraouf, Kyungtae Han, Rohit Gupta, Zihao Li, Ziran Wang
Ensuring traffic safety and preventing accidents is a critical goal in daily driving, where the advancement of computer vision technologies can be leveraged to achieve this goal.
no code implementations • 14 Apr 2023 • Liangqi Yuan, Yunsheng Ma, Lu Su, Ziran Wang
Naturalistic driving action recognition (NDAR) has proven to be an effective method for detecting driver distraction and reducing the risk of traffic accidents.
1 code implementation • 19 Sep 2022 • Yunsheng Ma, Ziran Wang
Ensuring traffic safety and mitigating accidents in modern driving is of paramount importance, and computer vision technologies have the potential to significantly contribute to this goal.
no code implementations • 12 Feb 2020 • Sicheng Zhao, Yunsheng Ma, Yang Gu, Jufeng Yang, Tengfei Xing, Pengfei Xu, Runbo Hu, Hua Chai, Kurt Keutzer
Emotion recognition in user-generated videos plays an important role in human-centered computing.
Ranked #4 on Video Emotion Recognition on Ekman6
1 code implementation • 17 Jun 2016 • Chang Liu, Yu Cao, Yan Luo, Guanling Chen, Vinod Vokkarane, Yunsheng Ma
We applied our proposed approach to two real-world food image data sets (UEC-256 and Food-101) and achieved impressive results.