MERL - Mitsubishi Electric Research Laboratories

Suhas Lohit

Suhas Lohit
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  • 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.

  • Recent News & Events

    •  NEWS    MERL researchers presenting four papers and co-organizing a workshop at CVPR 2023
      Date: June 18, 2023 - June 22, 2023
      Where: Vancouver/Canada
      MERL Contacts: Anoop Cherian; Michael J. Jones; Suhas Lohit; Kuan-Chuan Peng
      Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
      Brief
      • MERL researchers are presenting 4 papers and co-organizing a workshop at the CVPR 2023 conference, which will be held in Vancouver, Canada June 18-22. CVPR is one of the most prestigious and competitive international conferences in computer vision. Details are provided below.

        1. “Are Deep Neural Networks SMARTer than Second Graders,” by Anoop Cherian, Kuan-Chuan Peng, Suhas Lohit, Kevin Smith, and Joshua B. Tenenbaum

        We present SMART: a Simple Multimodal Algorithmic Reasoning Task and the associated SMART-101 dataset for evaluating the abstraction, deduction, and generalization abilities of neural networks in solving visuo-linguistic puzzles designed for children in the 6-8 age group. Our experiments using SMART-101 reveal that powerful deep models are not better than random accuracy when analyzed for generalization. We also evaluate large language models (including ChatGPT) on a subset of SMART-101 and find that while these models show convincing reasoning abilities, their answers are often incorrect.

        Paper: https://arxiv.org/abs/2212.09993

        2. “EVAL: Explainable Video Anomaly Localization,” by Ashish Singh, Michael J. Jones, and Erik Learned-Miller

        This work presents a method for detecting unusual activities in videos by building a high-level model of activities found in nominal videos of a scene. The high-level features used in the model are human understandable and include attributes such as the object class and the directions and speeds of motion. Such high-level features allow our method to not only detect anomalous activity but also to provide explanations for why it is anomalous.

        Paper: https://arxiv.org/abs/2212.07900

        3. "Aligning Step-by-Step Instructional Diagrams to Video Demonstrations," by Jiahao Zhang, Anoop Cherian, Yanbin Liu, Yizhak Ben-Shabat, Cristian Rodriguez, and Stephen Gould

        The rise of do-it-yourself (DIY) videos on the web has made it possible even for an unskilled person (or a skilled robot) to imitate and follow instructions to complete complex real world tasks. In this paper, we consider the novel problem of aligning instruction steps that are depicted as assembly diagrams (commonly seen in Ikea assembly manuals) with video segments from in-the-wild videos. We present a new dataset: Ikea Assembly in the Wild (IAW) and propose a contrastive learning framework for aligning instruction diagrams with video clips.

        Paper: https://arxiv.org/pdf/2303.13800.pdf

        4. "HaLP: Hallucinating Latent Positives for Skeleton-Based Self-Supervised Learning of Actions," by Anshul Shah, Aniket Roy, Ketul Shah, Shlok Kumar Mishra, David Jacobs, Anoop Cherian, and Rama Chellappa

        In this work, we propose a new contrastive learning approach to train models for skeleton-based action recognition without labels. Our key contribution is a simple module, HaLP: Hallucinating Latent Positives for contrastive learning. HaLP explores the latent space of poses in suitable directions to generate new positives. Our experiments using HaLP demonstrates strong empirical improvements.

        Paper: https://arxiv.org/abs/2304.00387

        The 4th Workshop on Fair, Data-Efficient, and Trusted Computer Vision

        MERL researcher Kuan-Chuan Peng is co-organizing the fourth Workshop on Fair, Data-Efficient, and Trusted Computer Vision (https://fadetrcv.github.io/2023/) in conjunction with CVPR 2023 on June 18, 2023. This workshop provides a focused venue for discussing and disseminating research in the areas of fairness, bias, and trust in computer vision, as well as adjacent domains such as computational social science and public policy.
    •  
    •  EVENT    MERL Contributes to ICASSP 2023
      Date: Sunday, June 4, 2023 - Saturday, June 10, 2023
      Location: Rhodes Island, Greece
      MERL Contacts: Petros T. Boufounos; Francois Germain; Toshiaki Koike-Akino; Jonathan Le Roux; Dehong Liu; Suhas Lohit; Yanting Ma; Hassan Mansour; Joshua Rapp; Anthony Vetro; Pu (Perry) Wang; Gordon Wichern
      Research Areas: Artificial Intelligence, Computational Sensing, Machine Learning, Signal Processing, Speech & Audio
      Brief
      • MERL has made numerous contributions to both the organization and technical program of ICASSP 2023, which is being held in Rhodes Island, Greece from June 4-10, 2023.

        Organization

        Petros Boufounos is serving as General Co-Chair of the conference this year, where he has been involved in all aspects of conference planning and execution.

        Perry Wang is the organizer of a special session on Radar-Assisted Perception (RAP), which will be held on Wednesday, June 7. The session will feature talks on signal processing and deep learning for radar perception, pose estimation, and mutual interference mitigation with speakers from both academia (Carnegie Mellon University, Virginia Tech, University of Illinois Urbana-Champaign) and industry (Mitsubishi Electric, Bosch, Waveye).

        Anthony Vetro is the co-organizer of the Workshop on Signal Processing for Autonomous Systems (SPAS), which will be held on Monday, June 5, and feature invited talks from leaders in both academia and industry on timely topics related to autonomous systems.

        Sponsorship

        MERL is proud to be a Silver Patron of the conference and will participate in the student job fair on Thursday, June 8. Please join this session to learn more about employment opportunities at MERL, including openings for research scientists, post-docs, and interns.

        MERL is pleased to be the sponsor of two IEEE Awards that will be presented at the conference. We congratulate Prof. Rabab Ward, the recipient of the 2023 IEEE Fourier Award for Signal Processing, and Prof. Alexander Waibel, the recipient of the 2023 IEEE James L. Flanagan Speech and Audio Processing Award.

        Technical Program

        MERL is presenting 13 papers in the main conference on a wide range of topics including source separation and speech enhancement, radar imaging, depth estimation, motor fault detection, time series recovery, and point clouds. One workshop paper has also been accepted for presentation on self-supervised music source separation.

        Perry Wang has been invited to give a keynote talk on Wi-Fi sensing and related standards activities at the Workshop on Integrated Sensing and Communications (ISAC), which will be held on Sunday, June 4.

        Additionally, Anthony Vetro will present a Perspective Talk on Physics-Grounded Machine Learning, which is scheduled for Thursday, June 8.

        About ICASSP

        ICASSP is the flagship conference of the IEEE Signal Processing Society, and the world's largest and most comprehensive technical conference focused on the research advances and latest technological development in signal and information processing. The event attracts more than 2000 participants each year.
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  • 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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  • MERL Publications

    •  Shenoy, V., Marks, T.K., Mansour, H., Lohit, S., "Unrolled IPPG: Video Heart Rate Esitmation via Unrolling Proximal Gradient Descent", IEEE International Conference on Image Processing (ICIP), September 2023.
      BibTeX TR2023-116 PDF
      • @inproceedings{Shenoy2023sep,
      • author = {Shenoy, Vineet and Marks, Tim K. and Mansour, Hassan and Lohit, Suhas},
      • title = {Unrolled IPPG: Video Heart Rate Esitmation via Unrolling Proximal Gradient Descent},
      • booktitle = {IEEE International Conference on Image Processing (ICIP)},
      • year = 2023,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2023-116}
      • }
    •  Jeon, E.S., Lohit, S., Anirudh, R., Turaga, P., "Robust Time Series Recovery and Classification Using Test-time Noise Simulator Networks", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2023.
      BibTeX TR2023-021 PDF Presentation
      • @inproceedings{Jeon2023may,
      • author = {Jeon, Eun Som and Lohit, Suhas and Anirudh, Rushil and Turaga, Pavan},
      • title = {Robust Time Series Recovery and Classification Using Test-time Noise Simulator Networks},
      • booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
      • year = 2023,
      • month = may,
      • url = {https://www.merl.com/publications/TR2023-021}
      • }
    •  Cherian, A., Peng, K.-C., Lohit, S., Smith, K., Tenenbaum, J.B., "Are Deep Neural Networks SMARTer than Second Graders?", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), March 2023, pp. 10834-10844.
      BibTeX TR2023-014 PDF Data Software Presentation
      • @inproceedings{Cherian2023mar,
      • author = {Cherian, Anoop and Peng, Kuan-Chuan and Lohit, Suhas and Smith, Kevin and Tenenbaum, Joshua B.},
      • title = {Are Deep Neural Networks SMARTer than Second Graders?},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2023,
      • pages = {10834--10844},
      • month = mar,
      • publisher = {CVF},
      • url = {https://www.merl.com/publications/TR2023-014}
      • }
    •  Wang, H., Lohit, S., Jones, M.J., Fu, R., "What Makes a “Good” Data Augmentation in Knowledge Distillation – A Statistical Perspective", Advances in Neural Information Processing Systems (NeurIPS), S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh, Eds., November 2022, pp. 13456-13469.
      BibTeX TR2022-147 PDF
      • @inproceedings{Wang2022nov,
      • author = {Wang, Huan and Lohit, Suhas and Jones, Michael J. and Fu, Raymond},
      • title = {What Makes a “Good” Data Augmentation in Knowledge Distillation – A Statistical Perspective},
      • booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
      • year = 2022,
      • editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},
      • pages = {13456--13469},
      • month = nov,
      • url = {https://www.merl.com/publications/TR2022-147}
      • }
    •  Romero, D., Lohit, S., "Learning Partial Equivariances from Data", Advances in Neural Information Processing Systems (NeurIPS), S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh, Eds., November 2022, pp. 36466-36478.
      BibTeX TR2022-148 PDF Presentation
      • @inproceedings{Romero2022nov,
      • author = {Romero, David and Lohit, Suhas},
      • title = {Learning Partial Equivariances from Data},
      • booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
      • year = 2022,
      • editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},
      • pages = {36466--36478},
      • month = nov,
      • url = {https://www.merl.com/publications/TR2022-148}
      • }
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  • 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}
      • }

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  • MERL Issued Patents

    • Title: "Systems and Methods for Multi-Spectral Image Fusion Using Unrolled Projected Gradient Descent and Convolutinoal Neural Network"
      Inventors: Liu, Dehong; Lohit, Suhas; Mansour, Hassan; Boufounos, Petros T.
      Patent No.: 10,891,527
      Issue Date: Jan 12, 2021
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