科学研究

研究方向

主动视觉计算与学习

 

可视信息是人类、智能机器进行日常生活和交流的关键,为后者提供理解和分析。因此,视觉计算和学习被运用在许多新兴技术领域,包括无人系统、增强现实、机器人操作和数字创作等。当前主动式视觉感知和学习正在成为研究趋势,智能体通过渐近式地与外界交互不断提升自身的感知和认知能力。

 

 

代表性科研成果

 

  • Xiangyu Kong, Bo Xin, Yizhou Wang, Gang Hua, “Collaborative Deep Reinforcement Learning for Joint Object Search,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, USA, July 21-26, 2017.
  •  Tianyang Zhao, Yifei Xu, Mathew Monfort, Wongun Choi, Chris Maker, Yibiao Zhao, Yizhou Wang, Yingnian Wu, "Multi-Agent Tensor Fusion for Contextual Trajectory Prediction," IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, USA, June 16-20, 2019.
  • Yingming Zuo, Weichao Qiu, Lingxi Xie, Fangwei Zhong, Yizhou Wang, Alan Yuille, "CRAVES: Controlling Robotic Arm With a Vision-Based Economic System," IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, USA, June 16-20, 2019.
  • Wenhan Luo, Peng Sun, Fangwei Zhong, Tong Zhang, Yizhou Wang, "End-to-end Active Object Tracking and Its Real-world Deployment via Reinforcement Learning," IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Vol. 22, No. 6, pp. 1634-1646, June 2020.
  • Siyan Dong, Kai Xu, Qiang Zhou, Andrea Tagliasacchi, Shiqing Xin, Matthias Nießner, Baoquan Chen, "Multi-Robot Collaborative Dense Scene Reconstruction," ACM Transactions on Graphics (TOG), Vol. 38, No. 4, pp. 84: 1-16, July 2019.

 

计算经济学

 

作为计算机和经济学的交叉方向,我们的兴趣在于社会经济科学,大规模市场设计,纳什均衡计算,多智能体信息系统,区块链经济等具体方向。在以上方向的研究中,我们将重点放在广义方法论和系统的开发中。我们用到的工具将包括博弈论,密码学,信息论,机器学习,统计,随机算法,理论经济学等。

 

 

代表性科研成果

 

  • Yuqing Kong, "Dominantly Truthful Multi-task Peer Prediction with a Constant Number of Tasks," ACM-SIAM Symposium on Discrete Algorithms (SODA), Salt Lake City, USA, January 5-8, 2020.
  • Xiaotie Deng, Tao Lin and Tao Xiao, "Private Data Manipulation in Optimal Sponsored Search Auction," the Web Conference (WWW), Taipei, April 20-24, 2020.

 

智能交互计算

 

智能交互计算通过研发新的计算理论方法与技术,赋能人、智能体(机器、智能软件系统)、与环境,提升人、智能体和环境间的协同合作功效。通过建立不同学科之间的桥梁,发展复合型学科,既服务于医疗健康、智能制造、经济金融、网络安全等国家重大需求,也惠及人们的日常生活与工作,如智慧城市、社交娱乐与人文艺术等。

 

 

代表性科研成果

 

  • Fangwei Zhong, Peng Sun, Wenhan Luo, Tingyun Yan, Yizhou Wang, "AD-VAT+: An Asymmetric Dueling Mechanism for Learning and Understanding Visual Active Tracking," IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2019.
  • Jing Li, Jing Xu, Fangwei Zhong, Xiangyu Kong, Yu Qiao and Yizhou Wang, "Pose-Assisted Multi-Camera Collaboration for Active Object Tracking," the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), New York, USA, February 7-12, 2020.
  • Yunhai Wang, Mingliang Xue, Yanyan Wang, Xinyuan Yan, Baoquan Chen, Chi-Wing Fu, Christophe Hurter, "Interactive Structure-aware Blending of Diverse Edge Bundling Visualizations," IEEE Transactions on Visualization and Computer Graphics (TVCG), Vol. 26, No. 1, pp. 687-696, January 2020.