Tim K. Marks

Tim K. Marks
Tim's Quick Links

  • Biography

    Prior to joining MERL's Imaging Group in 2008, Tim did postdoctoral research in robotic Simultaneous Localization and Mapping in collaboration with NASA's Jet Propulsion Laboratory. His research at MERL spans a variety of areas in computer vision and machine learning, including face recognition under variations in pose and lighting, and robotic vision and touch-based registration for industrial automation.

  • Recent News & Events

    •  NEWS    MERL Papers and Workshops at CVPR 2024
      Date: June 17, 2024 - June 21, 2024
      Where: Seattle, WA
      MERL Contacts: Petros T. Boufounos; Moitreya Chatterjee; Anoop Cherian; Michael J. Jones; Toshiaki Koike-Akino; Jonathan Le Roux; Suhas Lohit; Tim K. Marks; Pedro Miraldo; Jing Liu; Kuan-Chuan Peng; Pu (Perry) Wang; Ye Wang; Matthew Brand
      Research Areas: Artificial Intelligence, Computational Sensing, Computer Vision, Machine Learning, Speech & Audio
      Brief
      • MERL researchers are presenting 5 conference papers, 3 workshop papers, and are co-organizing two workshops at the CVPR 2024 conference, which will be held in Seattle, June 17-21. CVPR is one of the most prestigious and competitive international conferences in computer vision. Details of MERL contributions are provided below.

        CVPR Conference Papers:

        1. "TI2V-Zero: Zero-Shot Image Conditioning for Text-to-Video Diffusion Models" by H. Ni, B. Egger, S. Lohit, A. Cherian, Y. Wang, T. Koike-Akino, S. X. Huang, and T. K. Marks

        This work enables a pretrained text-to-video (T2V) diffusion model to be additionally conditioned on an input image (first video frame), yielding a text+image to video (TI2V) model. Other than using the pretrained T2V model, our method requires no ("zero") training or fine-tuning. The paper uses a "repeat-and-slide" method and diffusion resampling to synthesize videos from a given starting image and text describing the video content.

        Paper: https://www.merl.com/publications/TR2024-059
        Project page: https://merl.com/research/highlights/TI2V-Zero

        2. "Long-Tailed Anomaly Detection with Learnable Class Names" by C.-H. Ho, K.-C. Peng, and N. Vasconcelos

        This work aims to identify defects across various classes without relying on hard-coded class names. We introduce the concept of long-tailed anomaly detection, addressing challenges like class imbalance and dataset variability. Our proposed method combines reconstruction and semantic modules, learning pseudo-class names and utilizing a variational autoencoder for feature synthesis to improve performance in long-tailed datasets, outperforming existing methods in experiments.

        Paper: https://www.merl.com/publications/TR2024-040

        3. "Gear-NeRF: Free-Viewpoint Rendering and Tracking with Motion-aware Spatio-Temporal Sampling" by X. Liu, Y-W. Tai, C-T. Tang, P. Miraldo, S. Lohit, and M. Chatterjee

        This work presents a new strategy for rendering dynamic scenes from novel viewpoints. Our approach is based on stratifying the scene into regions based on the extent of motion of the region, which is automatically determined. Regions with higher motion are permitted a denser spatio-temporal sampling strategy for more faithful rendering of the scene. Additionally, to the best of our knowledge, ours is the first work to enable tracking of objects in the scene from novel views - based on the preferences of a user, provided by a click.

        Paper: https://www.merl.com/publications/TR2024-042

        4. "SIRA: Scalable Inter-frame Relation and Association for Radar Perception" by R. Yataka, P. Wang, P. T. Boufounos, and R. Takahashi

        Overcoming the limitations on radar feature extraction such as low spatial resolution, multipath reflection, and motion blurs, this paper proposes SIRA (Scalable Inter-frame Relation and Association) for scalable radar perception with two designs: 1) extended temporal relation, generalizing the existing temporal relation layer from two frames to multiple inter-frames with temporally regrouped window attention for scalability; and 2) motion consistency track with a pseudo-tracklet generated from observational data for better object association.

        Paper: https://www.merl.com/publications/TR2024-041

        5. "RILA: Reflective and Imaginative Language Agent for Zero-Shot Semantic Audio-Visual Navigation" by Z. Yang, J. Liu, P. Chen, A. Cherian, T. K. Marks, J. L. Roux, and C. Gan

        We leverage Large Language Models (LLM) for zero-shot semantic audio visual navigation. Specifically, by employing multi-modal models to process sensory data, we instruct an LLM-based planner to actively explore the environment by adaptively evaluating and dismissing inaccurate perceptual descriptions.

        Paper: https://www.merl.com/publications/TR2024-043

        CVPR Workshop Papers:

        1. "CoLa-SDF: Controllable Latent StyleSDF for Disentangled 3D Face Generation" by R. Dey, B. Egger, V. Boddeti, Y. Wang, and T. K. Marks

        This paper proposes a new method for generating 3D faces and rendering them to images by combining the controllability of nonlinear 3DMMs with the high fidelity of implicit 3D GANs. Inspired by StyleSDF, our model uses a similar architecture but enforces the latent space to match the interpretable and physical parameters of the nonlinear 3D morphable model MOST-GAN.

        Paper: https://www.merl.com/publications/TR2024-045

        2. “Tracklet-based Explainable Video Anomaly Localization” by A. Singh, M. J. Jones, and E. Learned-Miller

        This paper describes a new method for localizing anomalous activity in video of a scene given sample videos of normal activity from the same scene. The method is based on detecting and tracking objects in the scene and estimating high-level attributes of the objects such as their location, size, short-term trajectory and object class. These high-level attributes can then be used to detect unusual activity as well as to provide a human-understandable explanation for what is unusual about the activity.

        Paper: https://www.merl.com/publications/TR2024-057

        MERL co-organized workshops:

        1. "Multimodal Algorithmic Reasoning Workshop" by A. Cherian, K-C. Peng, S. Lohit, M. Chatterjee, H. Zhou, K. Smith, T. K. Marks, J. Mathissen, and J. Tenenbaum

        Workshop link: https://marworkshop.github.io/cvpr24/index.html

        2. "The 5th Workshop on Fair, Data-Efficient, and Trusted Computer Vision" by K-C. Peng, et al.

        Workshop link: https://fadetrcv.github.io/2024/

        3. "SuperLoRA: Parameter-Efficient Unified Adaptation for Large Vision Models" by X. Chen, J. Liu, Y. Wang, P. Wang, M. Brand, G. Wang, and T. Koike-Akino

        This paper proposes a generalized framework called SuperLoRA that unifies and extends different variants of low-rank adaptation (LoRA). Introducing new options with grouping, folding, shuffling, projection, and tensor decomposition, SuperLoRA offers high flexibility and demonstrates superior performance up to 10-fold gain in parameter efficiency for transfer learning tasks.

        Paper: https://www.merl.com/publications/TR2024-062
    •  
    •  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    MERL Researchers win Best Paper Award at ICCV 2019 Workshop on Statistical Deep Learning in Computer Vision
      Date: October 27, 2019
      Awarded to: Abhinav Kumar, Tim K. Marks, Wenxuan Mou, Chen Feng, Xiaoming Liu
      MERL Contact: Tim K. Marks
      Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
      Brief
      • MERL researcher Tim Marks, former MERL interns Abhinav Kumar and Wenxuan Mou, and MERL consultants Professor Chen Feng (NYU) and Professor Xiaoming Liu (MSU) received the Best Oral Paper Award at the IEEE/CVF International Conference on Computer Vision (ICCV) 2019 Workshop on Statistical Deep Learning in Computer Vision (SDL-CV) held in Seoul, Korea. Their paper, entitled "UGLLI Face Alignment: Estimating Uncertainty with Gaussian Log-Likelihood Loss," describes a method which, given an image of a face, estimates not only the locations of facial landmarks but also the uncertainty of each landmark location estimate.
    •  
    See All Awards for MERL

  • Research Highlights

  • Internships with Tim

    • CV0084: Internship - Vital signs from video using computer vision and AI

      MERL is seeking a highly motivated intern to conduct original research in estimating vital signs such as heart rate, heart rate variability, and blood pressure from video of a person. The successful candidate will use the latest methods in deep learning, computer vision, and signal processing to derive and implement new models, collect data, conduct experiments, and prepare results for publication, all in collaboration with MERL researchers. The candidate should be a Ph.D. student in computer vision with a strong publication record and experience in computer vision, signal processing, machine learning, and health monitoring. The successful candidate is expected to have published at least one paper in a top-tier computer vision or machine learning venue, such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS, or AAAI, and possess strong programming skills in Python and Pytorch. Start date is flexible; duration should be at least 3 months.

      Required Specific Experience

      • Ph.D. student in computer vision or related field.
      • Strong programming skills in Python and Pytorch.
      • Published at least one paper in a top-tier computer vision or machine learning venue, such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS, or AAAI.

    See All Internships at MERL

  • MERL Publications

    •  Cherian, A., Jain, S., Marks, T.K., "Few-shot Transparent Instance Segmentation for Bin Picking", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 2024.
      BibTeX TR2024-127 PDF
      • @inproceedings{Cherian2024sep,
      • author = {Cherian, Anoop and Jain, Siddarth and Marks, Tim K.}},
      • title = {Few-shot Transparent Instance Segmentation for Bin Picking},
      • booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
      • year = 2024,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2024-127}
      • }
    •  Yin, J., Luo, A., Du, Y., Cherian, A., Marks, T.K., Le Roux, J., Gan, C., "Disentangled Acoustic Fields For Multimodal Physical Scene Understanding", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 2024.
      BibTeX TR2024-125 PDF
      • @inproceedings{Yin2024sep,
      • author = {Yin, Jie and Luo, Andrew and Du, Yilun and Cherian, Anoop and Marks, Tim K. and Le Roux, Jonathan and Gan, Chuang}},
      • title = {Disentangled Acoustic Fields For Multimodal Physical Scene Understanding},
      • booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
      • year = 2024,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2024-125}
      • }
    •  Ni, H., Egger, B., Lohit, S., Cherian, A., Wang, Y., Koike-Akino, T., Huang, S.X., Marks, T.K., "TI2V-Zero: Zero-Shot Image Conditioning for Text-to-Video Diffusion Models", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2024, pp. 9015-9025.
      BibTeX TR2024-059 PDF Video Software Presentation
      • @inproceedings{Ni2024jun,
      • author = {Ni, Haomiao and Egger, Bernhard and Lohit, Suhas and Cherian, Anoop and Wang, Ye and Koike-Akino, Toshiaki and Huang, Sharon X. and Marks, Tim K.},
      • title = {TI2V-Zero: Zero-Shot Image Conditioning for Text-to-Video Diffusion Models},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2024,
      • pages = {9015--9025},
      • month = jun,
      • url = {https://www.merl.com/publications/TR2024-059}
      • }
    •  Dey, R., Egger, B., Boddeti, V., Wang, Y., Marks, T.K., "CoLa-SDF: Controllable Latent StyleSDF for Disentangled 3D Face Generation", IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), April 2024.
      BibTeX TR2024-045 PDF
      • @inproceedings{Dey2024apr,
      • author = {Dey, Rahul and Egger, Bernhard and Boddeti, Vishnu and Wang, Ye and Marks, Tim K.},
      • title = {CoLa-SDF: Controllable Latent StyleSDF for Disentangled 3D Face Generation},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
      • year = 2024,
      • month = apr,
      • url = {https://www.merl.com/publications/TR2024-045}
      • }
    •  Yang, Z., Liu, J., Chen, P., Cherian, A., Marks, T.K., Le Roux, J., Gan, C., "RILA: Reflective and Imaginative Language Agent for Zero-Shot Semantic Audio-Visual Navigation", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), April 2024, pp. 16251-16261.
      BibTeX TR2024-043 PDF
      • @inproceedings{Yang2024apr,
      • author = {Yang, Zeyuan and Liu, Jiageng and Chen, Peihao and Cherian, Anoop and Marks, Tim K. and Le Roux, Jonathan and Gan, Chuang},
      • title = {RILA: Reflective and Imaginative Language Agent for Zero-Shot Semantic Audio-Visual Navigation},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2024,
      • pages = {16251--16261},
      • month = apr,
      • publisher = {CVF},
      • url = {https://www.merl.com/publications/TR2024-043}
      • }
    See All MERL Publications for Tim

  • Other Publications

    •  Tim K Marks, Andrew Howard, Max Bajracharya, Garrison W Cottrell and Larry H Matthies, "Gamma-SLAM: Visual SLAM in unstructured environments using variance grid maps", Journal of Field Robotics, Vol. 26, No. 1, pp. 26-51, 2009.
      BibTeX
      • @Article{marks2009gamma,
      • author = {Marks, Tim K and Howard, Andrew and Bajracharya, Max and Cottrell, Garrison W and Matthies, Larry H},
      • title = {Gamma-SLAM: Visual SLAM in unstructured environments using variance grid maps},
      • journal = {Journal of Field Robotics},
      • year = 2009,
      • volume = 26,
      • number = 1,
      • pages = {26--51},
      • publisher = {Wiley Online Library}
      • }
    •  Luke Barrington, Tim K Marks, Janet Hui-wen Hsiao and Garrison W Cottrell, "NIMBLE: A kernel density model of saccade-based visual memory", Journal of Vision, Vol. 8, No. 14, 2008.
      BibTeX
      • @Article{barrington2008nimble,
      • author = {Barrington, Luke and Marks, Tim K and Hsiao, Janet Hui-wen and Cottrell, Garrison W},
      • title = {NIMBLE: A kernel density model of saccade-based visual memory},
      • journal = {Journal of Vision},
      • year = 2008,
      • volume = 8,
      • number = 14,
      • publisher = {Association for Research in Vision and Ophthalmology}
      • }
    •  Tim K Marks, Andrew Howard, Max Bajracharya, Garrison W Cottrell and Larry Matthies, "Gamma-SLAM: Using stereo vision and variance grid maps for SLAM in unstructured environments", Robotics and Automation, 2008. ICRA 2008. IEEE International Conference on, 2008, pp. 3717-3724.
      BibTeX
      • @Inproceedings{marks2008gamma,
      • author = {Marks, Tim K and Howard, Andrew and Bajracharya, Max and Cottrell, Garrison W and Matthies, Larry},
      • title = {Gamma-SLAM: Using stereo vision and variance grid maps for SLAM in unstructured environments},
      • booktitle = {Robotics and Automation, 2008. ICRA 2008. IEEE International Conference on},
      • year = 2008,
      • pages = {3717--3724},
      • organization = {IEEE}
      • }
    •  Lingyun Zhang, Matthew H Tong, Tim K Marks, Honghao Shan and Garrison W Cottrell, "SUN: A Bayesian framework for saliency using natural statistics", Journal of Vision, Vol. 8, No. 7, 2008.
      BibTeX
      • @Article{zhang2008sun,
      • author = {Zhang, Lingyun and Tong, Matthew H and Marks, Tim K and Shan, Honghao and Cottrell, Garrison W},
      • title = {SUN: A Bayesian framework for saliency using natural statistics},
      • journal = {Journal of Vision},
      • year = 2008,
      • volume = 8,
      • number = 7,
      • publisher = {Association for Research in Vision and Ophthalmology}
      • }
    •  Tim K Marks, Andrew Howard, Max Bajracharya, Garrison W Cottrell and Larry Matthies, "Gamma-SLAM: Stereo visual SLAM in unstructured environments using variance grid maps", IROS visual SLAM workshop, 2007.
      BibTeX
      • @Article{marks2007gamma,
      • author = {Marks, Tim K and Howard, Andrew and Bajracharya, Max and Cottrell, Garrison W and Matthies, Larry},
      • title = {Gamma-SLAM: Stereo visual SLAM in unstructured environments using variance grid maps},
      • journal = {IROS visual SLAM workshop},
      • year = 2007,
      • publisher = {Citeseer}
      • }
    •  Tim K Marks, John Hershey, J Cooper Roddey and Javier R Movellan, "Joint tracking of pose, expression, and texture using conditionally Gaussian filters", Advances in neural information processing systems, Vol. 17, pp. 889-896, 2005.
      BibTeX
      • @Article{marks2005joint,
      • author = {Marks, Tim K and Hershey, John and Roddey, J Cooper and Movellan, Javier R},
      • title = {Joint tracking of pose, expression, and texture using conditionally Gaussian filters},
      • journal = {Advances in neural information processing systems},
      • year = 2005,
      • volume = 17,
      • pages = {889--896}
      • }
    •  Tim K Marks, John Hershey, J Cooper Roddey and Javier R Movellan, "3d tracking of morphable objects using conditionally gaussian nonlinear filters", Computer Vision and Pattern Recognition Workshop, 2004. CVPRW'04. Conference on, 2004, pp. 190-190.
      BibTeX
      • @Inproceedings{marks20043d,
      • author = {Marks, Tim K and Hershey, John and Roddey, J Cooper and Movellan, Javier R},
      • title = {3d tracking of morphable objects using conditionally gaussian nonlinear filters},
      • booktitle = {Computer Vision and Pattern Recognition Workshop, 2004. CVPRW'04. Conference on},
      • year = 2004,
      • pages = {190--190},
      • organization = {IEEE}
      • }
    •  Tim K Marks and Javier R Movellan, "Diffusion networks, products of experts, and factor analysis", Proc. Int. Conf. on Independent Component Analysis, pp. 481-485, 2001.
      BibTeX
      • @Article{marks2001diffusion,
      • author = {Marks, Tim K and Movellan, Javier R},
      • title = {Diffusion networks, products of experts, and factor analysis},
      • journal = {Proc. Int. Conf. on Independent Component Analysis},
      • year = 2001,
      • pages = {481--485},
      • publisher = {Citeseer}
      • }

  • Software & Data Downloads

  • Videos

  • MERL Issued Patents

    • Title: "System and Method for Remote Measurements of Vital Signs of a Person in a Volatile Environment"
      Inventors: Marks, Tim; Mansour, Hassan; Nowara, Ewa; Nakamura, Yudai; Veeraraghavan, Ashok N.
      Patent No.: 12,056,879
      Issue Date: Aug 6, 2024
    • Title: "System and Method for Manipulating Two-Dimensional (2D) Images of Three-Dimensional (3D) Objects"
      Inventors: Marks, Tim; Medin, Safa; Cherian, Anoop; Wang, Ye
      Patent No.: 11,663,798
      Issue Date: May 30, 2023
    • Title: "InSeGAN: A Generative Approach to Instance Segmentation in Depth Images"
      Inventors: Cherian, Anoop; Pais, Goncalo; Marks, Tim; Sullivan, Alan
      Patent No.: 11,651,497
      Issue Date: May 16, 2023
    • Title: "Method and System for Scene-Aware Interaction"
      Inventors: Hori, Chiori; Cherian, Anoop; Chen, Siheng; Marks, Tim; Le Roux, Jonathan; Hori, Takaaki; Harsham, Bret A.; Vetro, Anthony; Sullivan, Alan
      Patent No.: 11,635,299
      Issue Date: Apr 25, 2023
    • Title: "Scene-Aware Video Encoder System and Method"
      Inventors: Cherian, Anoop; Hori, Chiori; Le Roux, Jonathan; Marks, Tim; Sullivan, Alan
      Patent No.: 11,582,485
      Issue Date: Feb 14, 2023
    • Title: "Low-latency Captioning System"
      Inventors: Hori, Chiori; Hori, Takaaki; Cherian, Anoop; Marks, Tim; Le Roux, Jonathan
      Patent No.: 11,445,267
      Issue Date: Sep 13, 2022
    • Title: "System and Method for a Dialogue Response Generation System"
      Inventors: Hori, Chiori; Cherian, Anoop; Marks, Tim; Hori, Takaaki
      Patent No.: 11,264,009
      Issue Date: Mar 1, 2022
    • Title: "System and Method for Remote Measurements of Vital Signs"
      Inventors: Marks, Tim; Mansour, Hassan; Nowara, Ewa; Nakamura, Yudai; Veeraraghavan, Ashok N.
      Patent No.: 11,259,710
      Issue Date: Mar 1, 2022
    • Title: "Image Processing System and Method for Landmark Location Estimation with Uncertainty"
      Inventors: Marks, Tim; Kumar, Abhinav; Mou, Wenxuan; Feng, Chen; Liu, Xiaoming
      Patent No.: 11,127,164
      Issue Date: Sep 21, 2021
    • Title: "Method and System for Determining 3D Object Poses and Landmark Points using Surface Patches"
      Inventors: Jones, Michael J.; Marks, Tim; Papazov, Chavdar
      Patent No.: 10,515,259
      Issue Date: Dec 24, 2019
    • Title: "Method and System for Multi-Modal Fusion Model"
      Inventors: Hori, Chiori; Hori, Takaaki; Hershey, John R.; Marks, Tim
      Patent No.: 10,417,498
      Issue Date: Sep 17, 2019
    • Title: "Method and System for Detecting Actions in Videos"
      Inventors: Jones, Michael J.; Marks, Tim; Tuzel, Oncel; Singh, Bharat
      Patent No.: 10,242,266
      Issue Date: Mar 26, 2019
    • Title: "Method and System for Detecting Actions in Videos using Contour Sequences"
      Inventors: Jones, Michael J.; Marks, Tim; Kulkarni, Kuldeep
      Patent No.: 10,210,391
      Issue Date: Feb 19, 2019
    • Title: "Method for Estimating Locations of Facial Landmarks in an Image of a Face using Globally Aligned Regression"
      Inventors: Tuzel, Oncel; Marks, Tim; Tambe, Salil
      Patent No.: 9,633,250
      Issue Date: Apr 25, 2017
    • Title: "Method for Generating Representations Polylines Using Piecewise Fitted Geometric Primitives"
      Inventors: Brand, Matthew E.; Marks, Tim; MV, Rohith
      Patent No.: 9,613,443
      Issue Date: Apr 4, 2017
    • Title: "Method for Determining Similarity of Objects Represented in Images"
      Inventors: Jones, Michael J.; Marks, Tim; Ahmed, Ejaz
      Patent No.: 9,436,895
      Issue Date: Sep 6, 2016
    • Title: "Method for Detecting 3D Geometric Boundaries in Images of Scenes Subject to Varying Lighting"
      Inventors: Marks, Tim; Tuzel, Oncel; Porikli, Fatih M.; Thornton, Jay E.; Ni, Jie
      Patent No.: 9,418,434
      Issue Date: Aug 16, 2016
    • Title: "Method for Factorizing Images of a Scene into Basis Images"
      Inventors: Tuzel, Oncel; Marks, Tim; Porikli, Fatih M.; Ni, Jie
      Patent No.: 9,384,553
      Issue Date: Jul 5, 2016
    • Title: "Method and System for Tracking People in Indoor Environments using a Visible Light Camera and a Low-Frame-Rate Infrared Sensor"
      Inventors: Marks, Tim; Jones, Michael J.; Kumar, Suren
      Patent No.: 9,245,196
      Issue Date: Jan 26, 2016
    • Title: "Method for Detecting and Tracking Objects in Image Sequences of Scenes Acquired by a Stationary Camera"
      Inventors: Marks, Tim; Jones, Michael J.; MV, Rohith
      Patent No.: 9,213,896
      Issue Date: Dec 15, 2015
    • Title: "Method and System for Segmenting Moving Objects from Images Using Foreground Extraction"
      Inventors: Veeraraghavan, Ashok N.; Marks, Tim; Taguchi, Yuichi
      Patent No.: 8,941,726
      Issue Date: Jan 27, 2015
    • Title: "Camera-Based 3D Climate Control"
      Inventors: Marks, Tim; Jones, Michael J.
      Patent No.: 8,929,592
      Issue Date: Jan 6, 2015
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