Suhas Lohit

Suhas Lohit
  • Biography

    Before coming to MERL, Suhas worked as an intern at MERL (summer 2018), SRI International (summer 2017) and Nvidia (summer 2016). His research interests include computer vision, computational imaging and deep learning. Recently, his research focus has been on creating hybrid model- and data-driven neural architectures for various applications in imaging and vision. He won the Best Paper Award at the CVPR workshop on Computational Cameras and Displays in 2015 and the University Graduate Fellowship at ASU for 2015-16.

  • Awards

    •  AWARD   Best Paper - Honorable Mention Award at WACV 2021
      Date: January 6, 2021
      Awarded to: Rushil Anirudh, Suhas Lohit, Pavan Turaga
      MERL Contact: Suhas Lohit
      Research Areas: Computational Sensing, Computer Vision, Machine Learning
      Brief
      • A team of researchers from Mitsubishi Electric Research Laboratories (MERL), Lawrence Livermore National Laboratory (LLNL) and Arizona State University (ASU) received the Best Paper Honorable Mention Award at WACV 2021 for their paper "Generative Patch Priors for Practical Compressive Image Recovery".

        The paper proposes a novel model of natural images as a composition of small patches which are obtained from a deep generative network. This is unlike prior approaches where the networks attempt to model image-level distributions and are unable to generalize outside training distributions. The key idea in this paper is that learning patch-level statistics is far easier. As the authors demonstrate, this model can then be used to efficiently solve challenging inverse problems in imaging such as compressive image recovery and inpainting even from very few measurements for diverse natural scenes.
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  • Internships with Suhas

    • CV1586: Cross-modal knowledge distillation

      MERL is seeking an intern to conduct research in the area of cross-modal knowledge distillation (RGB to IR, RGB to Lidar etc.) for applications in computer vision. The ideal candidate is a senior PhD student with experience in deep learning and computer vision and a good publication record at top-tier venues. Prior knowledge and experience with knowledge distillation and multiple modalities strongly preferred. Very good Python and Pytorch/Tensorflow skills are required. Publication of results in conference proceedings and journals is expected. The expected duration of the internship is 3 months and the start date is flexible. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

    See All Internships at MERL
  • MERL Publications

    •  Lohit, S., Anirudh, R., Turaga, P., "Recovering Trajectories of Unmarked Joints in 3D Human Actions Using Latent Space Optimization", IEEE Winter Conference on Applications of Computer Vision (WACV), January 2021.
      BibTeX TR2021-004 PDF
      • @inproceedings{Lohit2021jan,
      • author = {Lohit, Suhas and Anirudh, Rushil and Turaga, Pavan},
      • title = {Recovering Trajectories of Unmarked Joints in 3D Human Actions Using Latent Space Optimization},
      • booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
      • year = 2021,
      • month = jan,
      • url = {https://www.merl.com/publications/TR2021-004}
      • }
    •  Anirudh, R., Lohit, S., Turaga, P., "Generative Patch Priors for Practical Compressive Image Recovery", IEEE Winter Conference on Applications of Computer Vision (WACV), January 2021.
      BibTeX TR2021-003 PDF
      • @inproceedings{Anirudh2021jan,
      • author = {Anirudh, Rushil and Lohit, Suhas and Turaga, Pavan},
      • title = {Generative Patch Priors for Practical Compressive Image Recovery},
      • booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
      • year = 2021,
      • month = jan,
      • url = {https://www.merl.com/publications/TR2021-003}
      • }
    •  Wang, H., Lohit, S., Jones, M.J., Fu, R., "Knowledge Distillation Thrives on Data Augmentation", arXiv, December 2020.
      BibTeX arXiv
      • @article{Wang2020dec3,
      • author = {Wang, Huan and Lohit, Suhas and Jones, Michael J. and Fu, Raymond},
      • title = {Knowledge Distillation Thrives on Data Augmentation},
      • journal = {arXiv},
      • year = 2020,
      • month = dec,
      • url = {https://arxiv.org/abs/2012.02909}
      • }
    •  Wang, H., Lohit, S., Jones, M.J., Fu, R., "Multi-Head Knowledge Distillation for Model Compression", arXiv, December 2020.
      BibTeX arXiv
      • @article{Wang2020dec2,
      • author = {Wang, Huan and Lohit, Suhas and Jones, Michael J. and Fu, Raymond},
      • title = {Multi-Head Knowledge Distillation for Model Compression},
      • journal = {arXiv},
      • year = 2020,
      • month = dec,
      • url = {https://arxiv.org/abs/2012.02911}
      • }
    •  Anirudh, R., Lohit, S., Turaga, P., "Generative Patch Priors for Practical Compressive Image Recovery", arXiv, June 2020.
      BibTeX arXiv
      • @article{Anirudh2020jun,
      • author = {Anirudh, Rushil and Lohit, Suhas and Turaga, Pavan},
      • title = {Generative Patch Priors for Practical Compressive Image Recovery},
      • journal = {arXiv},
      • year = 2020,
      • month = jun,
      • url = {https://arxiv.org/abs/2006.10873}
      • }
    See All Publications for Suhas
  • Other Publications

    •  Suhas Lohit, Qiao Wang and Pavan Turaga, "Temporal Transformer Networks: Joint Learning of Invariant and Discriminative Time Warping", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 12426-12435.
      BibTeX
      • @Inproceedings{lohit2019temporal,
      • author = {Lohit, Suhas and Wang, Qiao and Turaga, Pavan},
      • title = {Temporal Transformer Networks: Joint Learning of Invariant and Discriminative Time Warping},
      • booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
      • year = 2019,
      • pages = {12426--12435}
      • }
    •  Suhas Lohit, Kuldeep Kulkarni, Ronan Kerviche, Pavan Turaga and Amit Ashok, "Convolutional neural networks for noniterative reconstruction of compressively sensed images", IEEE Transactions on Computational Imaging, Vol. 4, No. 3, pp. 326-340, 2018.
      BibTeX
      • @Article{lohit2018convolutional,
      • author = {Lohit, Suhas and Kulkarni, Kuldeep and Kerviche, Ronan and Turaga, Pavan and Ashok, Amit},
      • title = {Convolutional neural networks for noniterative reconstruction of compressively sensed images},
      • journal = {IEEE Transactions on Computational Imaging},
      • year = 2018,
      • volume = 4,
      • number = 3,
      • pages = {326--340},
      • publisher = {IEEE}
      • }
    •  Suhas Lohit, Ankan Bansal, Nitesh Shroff, Jaishanker Pillai, Pavan Turaga and Rama Chellappa, "Predicting Dynamical Evolution of Human Activities from a Single Image", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2018, pp. 383-392.
      BibTeX
      • @Inproceedings{lohit2018predicting,
      • author = {Lohit, Suhas and Bansal, Ankan and Shroff, Nitesh and Pillai, Jaishanker and Turaga, Pavan and Chellappa, Rama},
      • title = {Predicting Dynamical Evolution of Human Activities from a Single Image},
      • booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},
      • year = 2018,
      • pages = {383--392}
      • }
    •  Suhas Lohit and Pavan Turaga, "Learning invariant Riemannian geometric representations using deep nets", Proceedings of the IEEE International Conference on Computer Vision Workshops, 2017, pp. 1329-1338.
      BibTeX
      • @Inproceedings{lohit2017learning,
      • author = {Lohit, Suhas and Turaga, Pavan},
      • title = {Learning invariant Riemannian geometric representations using deep nets},
      • booktitle = {Proceedings of the IEEE International Conference on Computer Vision Workshops},
      • year = 2017,
      • pages = {1329--1338}
      • }
    •  Kuldeep Kulkarni, Suhas Lohit, Pavan Turaga, Ronan Kerviche and Amit Ashok, "Reconnet: Non-iterative reconstruction of images from compressively sensed measurements", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 449-458.
      BibTeX
      • @Inproceedings{kulkarni2016reconnet,
      • author = {Kulkarni, Kuldeep and Lohit, Suhas and Turaga, Pavan and Kerviche, Ronan and Ashok, Amit},
      • title = {Reconnet: Non-iterative reconstruction of images from compressively sensed measurements},
      • booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
      • year = 2016,
      • pages = {449--458}
      • }
    •  Suhas Lohit, Kuldeep Kulkarni and Pavan Turaga, "Direct inference on compressive measurements using convolutional neural networks", 2016 IEEE International Conference on Image Processing (ICIP), 2016, pp. 1913-1917.
      BibTeX
      • @Inproceedings{lohit2016direct,
      • author = {Lohit, Suhas and Kulkarni, Kuldeep and Turaga, Pavan},
      • title = {Direct inference on compressive measurements using convolutional neural networks},
      • booktitle = {2016 IEEE International Conference on Image Processing (ICIP)},
      • year = 2016,
      • pages = {1913--1917},
      • organization = {IEEE}
      • }
    •  Qiao Wang, Suhas Lohit, Meynard John Toledo, Matthew P Buman and Pavan Turaga, "A statistical estimation framework for energy expenditure of physical activities from a wrist-worn accelerometer", 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016, pp. 2631-2635.
      BibTeX
      • @Inproceedings{wang2016statistical,
      • author = {Wang, Qiao and Lohit, Suhas and Toledo, Meynard John and Buman, Matthew P and Turaga, Pavan},
      • title = {A statistical estimation framework for energy expenditure of physical activities from a wrist-worn accelerometer},
      • booktitle = {2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
      • year = 2016,
      • pages = {2631--2635},
      • organization = {IEEE}
      • }
    •  Suhas Lohit, Kuldeep Kulkarni, Pavan Turaga, Jian Wang and Aswin C Sankaranarayanan, "Reconstruction-free inference on compressive measurements", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2015, pp. 16-24.
      BibTeX
      • @Inproceedings{lohit2015reconstruction,
      • author = {Lohit, Suhas and Kulkarni, Kuldeep and Turaga, Pavan and Wang, Jian and Sankaranarayanan, Aswin C},
      • title = {Reconstruction-free inference on compressive measurements},
      • booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},
      • year = 2015,
      • pages = {16--24}
      • }