3 code implementations • 7 Jun 2019 • Ekim Yurtsever, Yongkang Liu, Jacob Lambert, Chiyomi Miyajima, Eijiro Takeuchi, Kazuya Takeda, John H. L. Hansen
The best result, with a 0. 937 AUC score, was obtained with the proposed network.
1 code implementation • 12 Mar 2022 • Zhengwei Bai, Guoyuan Wu, Matthew J. Barth, Yongkang Liu, Emrah Akin Sisbot, Kentaro Oguchi
3D object detection plays a fundamental role in enabling autonomous driving, which is regarded as the significant key to unlocking the bottleneck of contemporary transportation systems from the perspectives of safety, mobility, and sustainability.
1 code implementation • 1 Jul 2022 • Feiliang Ren, Yongkang Liu, Bochao Li, Shilei Liu, Bingchao Wang, Jiaqi Wang, Chunchao Liu, Qi Ma
In this paper, we propose an understanding-oriented machine reading comprehension model to address three kinds of robustness issues, which are over sensitivity, over stability and generalization.
1 code implementation • COLING 2022 • Yongkang Liu, Shi Feng, Daling Wang, Yifei Zhang
Building dialogue generation systems in a zero-shot scenario remains a huge challenge, since the typical zero-shot approaches in dialogue generation rely heavily on large-scale pre-trained language generation models such as GPT-3 and T5.
no code implementations • 17 Dec 2018 • Feiliang Ren, Yining Hou, Yan Li, Linfeng Pan, Yi Zhang, Xiaobo Liang, Yongkang Liu, Yu Guo, Rongsheng Zhao, Ruicheng Ming, Huiming Wu
In this work, we introduce TechKG, a large scale Chinese knowledge graph that is technology-oriented.
no code implementations • COLING 2018 • Feiliang Ren, Di Zhou, Zhihui Liu, Yongcheng Li, Rongsheng Zhao, Yongkang Liu, Xiaobo Liang
State-of-the-art methods usually concentrate on building deep neural networks based classification models on the training data in which the relations of the labeled entity pairs are given.
no code implementations • 23 Jun 2020 • Zhenyu Shou, Ziran Wang, Kyungtae Han, Yongkang Liu, Prashant Tiwari, Xuan Di
Behavior prediction plays an essential role in both autonomous driving systems and Advanced Driver Assistance Systems (ADAS), since it enhances vehicle's awareness of the imminent hazards in the surrounding environment.
no code implementations • 8 Jul 2020 • Yongkang Liu, Ziran Wang, Kyungtae Han, Zhenyu Shou, Prashant Tiwari, John H. L. Hansen
With the rapid development of intelligent vehicles and Advanced Driving Assistance Systems (ADAS), a mixed level of human driver engagements is involved in the transportation system.
no code implementations • 21 Dec 2020 • Yongkang Liu, Shi Feng, Daling Wang, Kaisong Song, Feiliang Ren, Yifei Zhang
We investigate response selection for multi-turn conversation in retrieval-based chatbots.
no code implementations • 7 Dec 2021 • Yongkang Liu, Ziran Wang, Kyungtae Han, Zhenyu Shou, Prashant Tiwari, John H. L. Hansen
To advance the development of visual guidance systems, we introduce a novel vision-cloud data fusion methodology, integrating camera image and Digital Twin information from the cloud to help intelligent vehicles make better decisions.
no code implementations • 24 Jan 2022 • Zhengwei Bai, Guoyuan Wu, Xuewei Qi, Yongkang Liu, Kentaro Oguchi, Matthew J. Barth
In this study, a \textit{Cyber Mobility Mirror (CMM)} Co-Simulation Platform is designed for enabling CDA by providing authentic perception information.
no code implementations • 28 Jan 2022 • Zhengwei Bai, Guoyuan Wu, Xuewei Qi, Yongkang Liu, Kentaro Oguchi, Matthew J. Barth
Object detection plays a fundamental role in enabling Cooperative Driving Automation (CDA), which is regarded as the revolutionary solution to addressing safety, mobility, and sustainability issues of contemporary transportation systems.
no code implementations • 28 Feb 2022 • Zhengwei Bai, Saswat Priyadarshi Nayak, Xuanpeng Zhao, Guoyuan Wu, Matthew J. Barth, Xuewei Qi, Yongkang Liu, Emrah Akin Sisbot, Kentaro Oguchi
Object perception plays a fundamental role in Cooperative Driving Automation (CDA) which is regarded as a revolutionary promoter for the next-generation transportation systems.
no code implementations • 28 Feb 2022 • Zhensong Wei, Xuewei Qi, Zhengwei Bai, Guoyuan Wu, Saswat Nayak, Peng Hao, Matthew Barth, Yongkang Liu, Kentaro Oguchi
The current challenges of this solution are how to effectively combine different perception tasks into a single backbone and how to efficiently learn the spatiotemporal features directly from point cloud sequences.
no code implementations • 25 Feb 2022 • Feiliang Ren, Yongkang Liu, Bochao Li, Zhibo Wang, Yu Guo, Shilei Liu, Huimin Wu, Jiaqi Wang, Chunchao Liu, Bingchao Wang
Most existing multi-document machine reading comprehension models mainly focus on understanding the interactions between the input question and documents, but ignore following two kinds of understandings.
no code implementations • 22 Aug 2022 • Zhengwei Bai, Guoyuan Wu, Matthew J. Barth, Yongkang Liu, Emrah Akin Sisbot, Kentaro Oguchi, Zhitong Huang
Perceiving the environment is one of the most fundamental keys to enabling Cooperative Driving Automation (CDA), which is regarded as the revolutionary solution to addressing the safety, mobility, and sustainability issues of contemporary transportation systems.
no code implementations • 21 Oct 2022 • Shiqi Liu, Mengdi Xu, Piede Huang, Yongkang Liu, Kentaro Oguchi, Ding Zhao
Continual reinforcement learning aims to sequentially learn a variety of tasks, retaining the ability to perform previously encountered tasks while simultaneously developing new policies for novel tasks.
no code implementations • 25 Oct 2022 • Yongkang Liu, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang
Current end-to-end retrieval-based dialogue systems are mainly based on Recurrent Neural Networks or Transformers with attention mechanisms.
no code implementations • 14 Dec 2022 • Zhengwei Bai, Guoyuan Wu, Matthew J. Barth, Yongkang Liu, Emrah Akin Sisbot, Kentaro Oguchi
Utilizing the latest advances in Artificial Intelligence (AI), the computer vision community is now witnessing an unprecedented evolution in all kinds of perception tasks, particularly in object detection.
no code implementations • 18 Dec 2022 • Yongkang Liu, Shi Feng, Daling Wang, Yifei Zhang, Hinrich Schütze
We investigate response generation for multi-turn dialogue in generative-based chatbots.
no code implementations • 6 Feb 2023 • Zhengwei Bai, Guoyuan Wu, Matthew J. Barth, Yongkang Liu, Emrah Akin Sisbot, Kentaro Oguchi
A Dynamic Feature Sharing (DFS) methodology is introduced to support this CP system under certain constraints and a Random Priority Filtering (RPF) method is proposed to conduct DFS with high performance.
no code implementations • 24 May 2023 • Yongkang Liu, Shi Feng, Daling Wang, Yifei Zhang, Hinrich Schütze
There are risks in using eference-free evaluators based on LLMs to evaluate the quality of dialogue responses.
no code implementations • 26 Jan 2024 • Yongkang Liu, Yiqun Zhang, Qian Li, Tong Liu, Shi Feng, Daling Wang, Yifei Zhang, Hinrich Schütze
As LMs grow in size, fine-tuning the full parameters of LMs requires a prohibitively large amount of GPU memory.