Ruohan Gao 高若涵

Ph.D. Student
Department of Computer Science
The University of Texas at Austin
Email: rhgao[AT]cs[DOT]utexas[DOT]edu
Office: GDC 4.728F

Short Bio

Ruohan Gao is a fourth-year Ph.D. student under the supervision of Prof. Kristen Grauman in the Department of Computer Science at The University of Texas at Austin. His research interests are in computer vision, machine learning and data mining. Particularly, he is interested in learning from unlabeled videos by leveraging multiple modalities.

Ruohan received his B.Eng. degree from the Department of Information Engineering at The Chinese University of Hong Kong (CUHK) in 2015 with First Class Honours. At CUHK, he conducted research on large graph mining through graph sampling under the supervision of Prof. Wing Cheong Lau.

Ruohan's research has been supported by Google Fellowship and Adobe Fellowship.

News

  • [March, 2019] I've been awarded the 2019 Google PhD Fellowship in Machine Perception, Speech Technology and Computer Vision. Thanks, Google!
  • [November, 2018] I've been awarded the 2019 Adobe Research Fellowship. Thanks, Adobe!
  • [August, 2018] I spent a wonderful summer interning at Facebook AI Research (FAIR).
  • [January, 2016] I started to work on computer vision and joined UT-Austin Computer Vision Group. I am luckily advised by Prof. Kristen Grauman.
  • [July, 2015] I graduated from CUHK with First Class Honours and I am going to UT Austin this fall to pursue my Ph.D. degree in Computer Science. I look forward to the challenges and opportunities that await.

    • Talks

  • Learning to Separate Object Sounds by Watching Unlabeled Video, ECCV'18 Oral, Munich, Germany (Video, PDF, PPT)

  • Im2Flow: Motion Hallucination from Static Images for Action Recognition, CVPR'18 Oral, Salt Lake City (Video, PDF)

  • Learning to Separate Object Sounds by Watching Unlabeled Video at the Sight and Sound Workshop, CVPR'18, Salt Lake City (PDF, PPT)

    • Publications



    Co-Separating Sounds of Visual Objects

    Ruohan Gao and Kristen Grauman.
    arXiv preprint, 2019.

    arXiv Project Page






    2.5D Visual Sound

    Ruohan Gao and Kristen Grauman.
    Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
    (Oral Presentation)
    PDF Supp Project Page FAIR-Play Dataset Media Coverage




    Learning to Separate Object Sounds by Watching Unlabeled Video

    Ruohan Gao, Rogerio Feris, Kristen Grauman.
    European Conference on Computer Vision (ECCV), 2018.
    (Oral Presentation)
    PDF Supp Poster Project Page Oral Video




    ShapeCodes: Self-Supervised Feature Learning by Lifting Views to Viewgrids

    Dinesh Jayaraman, Ruohan Gao, Kristen Grauman.
    European Conference on Computer Vision (ECCV), 2018.
    PDF Supp




    Im2Flow: Motion Hallucination from Static Images for Action Recognition

    Ruohan Gao, Bo Xiong, Kristen Grauman.
    Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
    (Oral Presentation)
    PDF Supp Poster Project Page Code Oral Video








    On-Demand Learning for Deep Image Restoration

    Ruohan Gao and Kristen Grauman.
    International Conference on Computer Vision (ICCV), 2017.

    PDF Supp Poster Project Page Code









    Object-Centric Representation Learning from Unlabeled Videos

    Ruohan Gao, Dinesh Jayaraman, Kristen Grauman.
    Asian Conference on Computer Vision (ACCV), 2016.

    PDF Poster Project Page





    Media Coverage

  • MIT Technology Review: Deep learning turns mono recordings into immersive sound.

  • Two Minute Papers: This AI produces binaural (2.5D) audio.

    • Undergraduate Research Publications

    Ruohan Gao, Huanle Xu, Pili Hu, Wing Cheong Lau, “Accelerating Graph Mining Algorithms via Uniform Random Edge Sampling”, IEEE ICC, 2016. [PDF]

    Ruohan Gao, Pili Hu, Wing Cheong Lau, “Graph Property Preservation under Community-Based Sampling”, IEEE Globecom, 2015. [PDF]

    Ruohan Gao, Huanle Xu, Pili Hu, Wing Cheong Lau, “Accelerating Graph Mining Algorithms via Uniform Random Edge Sampling (Poster)”, ACM Conference on Online Social Networks (COSN), 2015.