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

    •  TALK    [MERL Seminar Series 2024] Melanie Mitchell presents talk titled "The Debate Over 'Understanding' in AI's Large Language Models"
      Date & Time: Tuesday, February 13, 2024; 1:00 PM
      Speaker: Melanie Mitchell, Santa Fe Institute
      MERL Host: Suhas Lohit
      Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Human-Computer Interaction
      Abstract
      • I will survey a current, heated debate in the AI research community on whether large pre-trained language models can be said to "understand" language -- and the physical and social situations language encodes -- in any important sense. I will describe arguments that have been made for and against such understanding, and, more generally, will discuss what methods can be used to fairly evaluate understanding and intelligence in AI systems. I will conclude with key questions for the broader sciences of intelligence that have arisen in light of these discussions.
    •  
    •  NEWS    MERL researchers presenting four papers and organizing the VLAR-SMART101 Workshop at ICCV 2023
      Date: October 2, 2023 - October 6, 2023
      Where: Paris/France
      MERL Contacts: Moitreya Chatterjee; Anoop Cherian; Michael J. Jones; Toshiaki Koike-Akino; Suhas Lohit; Tim K. Marks; Pedro Miraldo; Kuan-Chuan Peng; Ye Wang
      Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
      Brief
      • MERL researchers are presenting 4 papers and organizing the VLAR-SMART-101 workshop at the ICCV 2023 conference, which will be held in Paris, France October 2-6. ICCV is one of the most prestigious and competitive international conferences in computer vision. Details are provided below.

        1. Conference paper: “Steered Diffusion: A Generalized Framework for Plug-and-Play Conditional Image Synthesis,” by Nithin Gopalakrishnan Nair, Anoop Cherian, Suhas Lohit, Ye Wang, Toshiaki Koike-Akino, Vishal Patel, and Tim K. Marks

        Conditional generative models typically demand large annotated training sets to achieve high-quality synthesis. As a result, there has been significant interest in plug-and-play generation, i.e., using a pre-defined model to guide the generative process. In this paper, we introduce Steered Diffusion, a generalized framework for fine-grained photorealistic zero-shot conditional image generation using a diffusion model trained for unconditional generation. The key idea is to steer the image generation of the diffusion model during inference via designing a loss using a pre-trained inverse model that characterizes the conditional task. Our model shows clear qualitative and quantitative improvements over state-of-the-art diffusion-based plug-and-play models, while adding negligible computational cost.

        2. Conference paper: "BANSAC: A dynamic BAyesian Network for adaptive SAmple Consensus," by Valter Piedade and Pedro Miraldo

        We derive a dynamic Bayesian network that updates individual data points' inlier scores while iterating RANSAC. At each iteration, we apply weighted sampling using the updated scores. Our method works with or without prior data point scorings. In addition, we use the updated inlier/outlier scoring for deriving a new stopping criterion for the RANSAC loop. Our method outperforms the baselines in accuracy while needing less computational time.

        3. Conference paper: "Robust Frame-to-Frame Camera Rotation Estimation in Crowded Scenes," by Fabien Delattre, David Dirnfeld, Phat Nguyen, Stephen Scarano, Michael J. Jones, Pedro Miraldo, and Erik Learned-Miller

        We present a novel approach to estimating camera rotation in crowded, real-world scenes captured using a handheld monocular video camera. Our method uses a novel generalization of the Hough transform on SO3 to efficiently find the camera rotation most compatible with the optical flow. Because the setting is not addressed well by other data sets, we provide a new dataset and benchmark, with high-accuracy and rigorously annotated ground truth on 17 video sequences. Our method is more accurate by almost 40 percent than the next best method.

        4. Workshop paper: "Tensor Factorization for Leveraging Cross-Modal Knowledge in Data-Constrained Infrared Object Detection" by Manish Sharma*, Moitreya Chatterjee*, Kuan-Chuan Peng, Suhas Lohit, and Michael Jones

        While state-of-the-art object detection methods for RGB images have reached some level of maturity, the same is not true for Infrared (IR) images. The primary bottleneck towards bridging this gap is the lack of sufficient labeled training data in the IR images. Towards addressing this issue, we present TensorFact, a novel tensor decomposition method which splits the convolution kernels of a CNN into low-rank factor matrices with fewer parameters. This compressed network is first pre-trained on RGB images and then augmented with only a few parameters. This augmented network is then trained on IR images, while freezing the weights trained on RGB. This prevents it from over-fitting, allowing it to generalize better. Experiments show that our method outperforms state-of-the-art.

        5. “Vision-and-Language Algorithmic Reasoning (VLAR) Workshop and SMART-101 Challenge” by Anoop Cherian,  Kuan-Chuan Peng, Suhas Lohit, Tim K. Marks, Ram Ramrakhya, Honglu Zhou, Kevin A. Smith, Joanna Matthiesen, and Joshua B. Tenenbaum

        MERL researchers along with researchers from MIT, GeorgiaTech, Math Kangaroo USA, and Rutgers University are jointly organizing a workshop on vision-and-language algorithmic reasoning at ICCV 2023 and conducting a challenge based on the SMART-101 puzzles described in the paper: Are Deep Neural Networks SMARTer than Second Graders?. A focus of this workshop is to bring together outstanding faculty/researchers working at the intersections of vision, language, and cognition to provide their opinions on the recent breakthroughs in large language models and artificial general intelligence, as well as showcase their cutting edge research that could inspire the audience to search for the missing pieces in our quest towards solving the puzzle of artificial intelligence.

        Workshop link: https://wvlar.github.io/iccv23/
    •  

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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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  • Research Highlights

  • MERL Publications

    •  Hegde, D., Lohit, S., Peng, K.-C., Jones, M.J., Patel, V.M., "Equivariant Spatio-Temporal Self-Supervision for LiDAR Object Detection", arXiv, April 2024.
      BibTeX arXiv
      • @article{Hegde2024apr2,
      • author = {Hegde, Deepti and Lohit, Suhas and Peng, Kuan-Chuan and Jones, Michael J. and Patel, Vishal M.},
      • title = {Equivariant Spatio-Temporal Self-Supervision for LiDAR Object Detection},
      • journal = {arXiv},
      • year = 2024,
      • month = apr,
      • url = {https://arxiv.org/abs/2404.11737}
      • }
    •  Hegde, D., Lohit, S., Peng, K.-C., Jones, M.J., Patel, V.M., "Multimodal 3D Object Detection on Unseen Domains", arXiv, April 2024.
      BibTeX arXiv
      • @article{Hegde2024apr,
      • author = {Hegde, Deepti and Lohit, Suhas and Peng, Kuan-Chuan and Jones, Michael J. and Patel, Vishal M.},
      • title = {Multimodal 3D Object Detection on Unseen Domains},
      • journal = {arXiv},
      • year = 2024,
      • month = apr,
      • url = {https://arxiv.org/abs/2404.11764}
      • }
    •  Basu, S., Lohit, S., Brand, M., "G-RepsNet: A Fast and General Construction of Equivariant Networks for Arbitrary Matrix Groups", arXiv, February 2024.
      BibTeX arXiv
      • @article{Basu2024feb,
      • author = {Basu, Sourya and Lohit, Suhas and Brand, Matthew},
      • title = {G-RepsNet: A Fast and General Construction of Equivariant Networks for Arbitrary Matrix Groups},
      • journal = {arXiv},
      • year = 2024,
      • month = feb,
      • url = {https://arxiv.org/abs/2402.15413}
      • }
    •  Carmichael, Z., Jones, L.S., Cherian, A., Michael J., , Scheirer, W., "Pixel-Grounded Prototypical Part Networks", IEEE Winter Conference on Applications of Computer Vision (WACV), January 2024.
      BibTeX TR2024-002 PDF Presentation
      • @inproceedings{Carmichael2024jan,
      • author = {Carmichael, Zachariah and Jones, Lohit, Suhas and Cherian, Anoop and Michael J. and Scheirer, Walter},
      • title = {Pixel-Grounded Prototypical Part Networks},
      • booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
      • year = 2024,
      • month = jan,
      • url = {https://www.merl.com/publications/TR2024-002}
      • }
    •  Sharma, M., Chatterjee, M., Peng, K.-C., Lohit, S., Jones, M.J., "Tensor Factorization for Leveraging Cross-Modal Knowledge in Data-Constrained Infrared Object Detection", IEEE International Conference on Computer Vision Workshops (ICCV), October 2023, pp. 924-932.
      BibTeX TR2023-125 PDF Presentation
      • @inproceedings{Sharma2023oct,
      • author = {Sharma, Manish and Chatterjee, Moitreya and Peng, Kuan-Chuan and Lohit, Suhas and Jones, Michael J.},
      • title = {Tensor Factorization for Leveraging Cross-Modal Knowledge in Data-Constrained Infrared Object Detection},
      • booktitle = {IEEE International Conference on Computer Vision Workshops (ICCV)},
      • year = 2023,
      • pages = {924--932},
      • month = oct,
      • url = {https://www.merl.com/publications/TR2023-125}
      • }
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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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