Siheng Chen

Siheng Chen
  • Biography

    Before coming to MERL, Siheng worked postdoctoral research associate at CMU and on perception and prediction systems for self-driving cars at Uber Advanced Technologies Group. At CMU he received 2 masters degrees (one in Electrical \& Computer Engineering and one in Machine Learning) in addition to his PhD. He received his bachelor's degree in Electronics Engineering in 2011 from Beijing Institute of Technology, China. He is the recipient of the 2018 IEEE Signal Processing Society Young Author Best Paper Award. His coauthored paper received the Best Student Paper Award at 2018 IEEE Global Conference on Signal and Information Processing. His research interests include graph signal processing, graph neural networks, 3D point cloud processing, and graph mining.

  • Recent News & Events

    •  NEWS   MERL's Scene-Aware Interaction Technology Featured in Mitsubishi Electric Corporation Press Release
      Date: July 22, 2020
      Where: Tokyo, Japan
      MERL Contacts: Siheng Chen; Anoop Cherian; Bret Harsham; Chiori Hori; Takaaki Hori; Jonathan Le Roux; Tim Marks; Alan Sullivan; Anthony Vetro
      Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Speech & Audio
      Brief
      • Mitsubishi Electric Corporation announced that the company has developed what it believes to be the world’s first technology capable of highly natural and intuitive interaction with humans based on a scene-aware capability to translate multimodal sensing information into natural language.

        The novel technology, Scene-Aware Interaction, incorporates Mitsubishi Electric’s proprietary Maisart® compact AI technology to analyze multimodal sensing information for highly natural and intuitive interaction with humans through context-dependent generation of natural language. The technology recognizes contextual objects and events based on multimodal sensing information, such as images and video captured with cameras, audio information recorded with microphones, and localization information measured with LiDAR.

        Scene-Aware Interaction for car navigation, one target application, will provide drivers with intuitive route guidance. The technology is also expected to have applicability to human-machine interfaces for in-vehicle infotainment, interaction with service robots in building and factory automation systems, systems that monitor the health and well-being of people, surveillance systems that interpret complex scenes for humans and encourage social distancing, support for touchless operation of equipment in public areas, and much more. The technology is based on recent research by MERL's Speech & Audio and Computer Vision groups.


        Demonstration Video:



        Link:

        Mitsubishi Electric Corporation Press Release
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    •  NEWS   MERL researchers presenting four papers and organizing two workshops at CVPR 2020 conference
      Date: June 14, 2020 - June 19, 2020
      MERL Contacts: Siheng Chen; Anoop Cherian; Michael Jones; Toshiaki Koike-Akino; Tim Marks; Kuan-Chuan Peng; Ye Wang
      Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
      Brief
      • MERL researchers are presenting four papers (two oral papers and two posters) and organizing two workshops at the IEEE/CVF Computer Vision and Pattern Recognition (CVPR 2020) conference.

        CVPR 2020 Orals with MERL authors:
        1. "Dynamic Multiscale Graph Neural Networks for 3D Skeleton Based Human Motion Prediction," by Maosen Li, Siheng Chen, Yangheng Zhao, Ya Zhang, Yanfeng Wang, Qi Tian
        2. "Collaborative Motion Prediction via Neural Motion Message Passing," by Yue Hu, Siheng Chen, Ya Zhang, Xiao Gu

        CVPR 2020 Posters with MERL authors:
        3. "LUVLi Face Alignment: Estimating Landmarks’ Location, Uncertainty, and Visibility Likelihood," by Abhinav Kumar, Tim K. Marks, Wenxuan Mou, Ye Wang, Michael Jones, Anoop Cherian, Toshiaki Koike-Akino, Xiaoming Liu, Chen Feng
        4. "MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird’s Eye View Maps," by Pengxiang Wu, Siheng Chen, Dimitris N. Metaxas

        CVPR 2020 Workshops co-organized by MERL researchers:
        1. Fair, Data-Efficient and Trusted Computer Vision
        2. Deep Declarative Networks.
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  • Awards

    •  AWARD   MERL researcher wins IEEE Young Author Best Paper award
      Date: January 2, 2019
      Awarded to: Siheng Chen
      MERL Contact: Siheng Chen
      Research Area: Signal Processing
      Brief
      • MERL researcher, Siheng Chen, has won an IEEE Young Author Best Paper award for his paper entitled "Discrete Signal Processing on Graphs: Sampling Theory". This paper, published in the December 2015 issue of IEEE Transactions on Signal Processing, proposes a sampling theory for signals that are supported on either directed or undirected graphs. The theory follows the same paradigm as classical sampling theory and shows that perfect recovery is possible for graph signals bandlimited under the graph Fourier transform. The award honors the authors of an especially meritorious paper dealing with a subject related to IEEE's technical scope and appearing in one if its journals within a three year window of eligibility.
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  • MERL Publications

    •  Liu, D., Chen, S., Boufounos, P.T., "Graph-Based Array Signal Denoising for Perturbed Synthetic Aperture Radar", IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 2020.
      BibTeX TR2020-114 PDF Video
      • @inproceedings{Liu2020jul,
      • author = {Liu, Dehong and Chen, Siheng and Boufounos, Petros T.},
      • title = {Graph-Based Array Signal Denoising for Perturbed Synthetic Aperture Radar},
      • booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
      • year = 2020,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-114}
      • }
    •  Chen, S., Li, M., Zhang, Y., "Sampling and Recovery of Graph Signals based on Graph Neural Networks", arXiv, July 2020.
      BibTeX
      • @article{Chen2020jul,
      • author = {Chen, Siheng and Li, Maosen and Zhang, Ya},
      • title = {Sampling and Recovery of Graph Signals based on Graph Neural Networks},
      • journal = {arXiv},
      • year = 2020,
      • month = jul
      • }
    •  Hu, Y., Chen, S., Zhang, Y., Gu, X., "Collaborative Motion Prediction via Neural Motion Message Passing", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2020.
      BibTeX TR2020-072 PDF
      • @inproceedings{Hu2020jun,
      • author = {Hu, Yue and Chen, Siheng and Zhang, Ya and Gu, Xiao},
      • title = {Collaborative Motion Prediction via Neural Motion Message Passing},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2020,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2020-072}
      • }
    •  Li, M., Chen, S., Zhao, Y., Zhang, Y., Wang, Y., Tia, Q., "Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Prediction", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2020.
      BibTeX TR2020-073 PDF
      • @inproceedings{Li2020jun,
      • author = {Li, Maosen and Chen, Sihen and Zhao, Yangheng and Zhang, Ya and Wang, Yanfeng and Tia, Qi},
      • title = {Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Prediction},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2020,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2020-073}
      • }
    •  Chen, S., Zhao, L., Eldar, Y., "Graph Unrolling Networks: Interpretable Neural Networks for Graph Signal Denoising", arXiv, June 2020.
      BibTeX
      • @article{Chen2020jun,
      • author = {Chen, Siheng and Zhao, Lingxiao and Eldar, Yonina},
      • title = {Graph Unrolling Networks: Interpretable Neural Networks for Graph Signal Denoising},
      • journal = {arXiv},
      • year = 2020,
      • month = jun
      • }
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  • Software Downloads

  • MERL Issued Patents

    • Title: "Methods and Systems for Fast Resampling Method and Apparatus for Point Cloud Data"
      Inventors: Tian, Dong; Feng, Chen; Vetro, Anthony; Chen, Siheng
      Patent No.: 10,229,533
      Issue Date: Mar 12, 2019
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