Michael J. Jones

Michael J. Jones
Mike's Quick Links

  • Biography

    Mike's main areas of interest are computer vision, machine learning and data mining. He has focused on algorithms for detecting and analyzing people in images and video including face detection and recognition and pedestrian detection. He is a co-inventor of the popular Viola-Jones face detection method. Mike has been awarded the Marr Prize at ICCV and the Longuet-Higgins Prize at CVPR.

  • Recent News & Events

    •  NEWS    MERL Papers and Workshops at CVPR 2025
      Date: June 11, 2025 - June 15, 2025
      Where: Nashville, TN, USA
      MERL Contacts: Matthew Brand; Moitreya Chatterjee; Anoop Cherian; François Germain; Michael J. Jones; Toshiaki Koike-Akino; Jing Liu; Suhas Lohit; Tim K. Marks; Pedro Miraldo; Kuan-Chuan Peng; Naoko Sawada; Pu (Perry) Wang; Ye Wang
      Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Signal Processing, Speech & Audio
      Brief
      • MERL researchers are presenting 2 conference papers, co-organizing two workshops, and presenting 7 workshop papers at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2025 conference, which will be held in Nashville, TN, USA from June 11-15, 2025. CVPR is one of the most prestigious and competitive international conferences in the area of computer vision. Details of MERL contributions are provided below:


        Main Conference Papers:

        1. "UWAV: Uncertainty-weighted Weakly-supervised Audio-Visual Video Parsing" by Y.H. Lai, J. Ebbers, Y. F. Wang, F. Germain, M. J. Jones, M. Chatterjee

        This work deals with the task of weakly‑supervised Audio-Visual Video Parsing (AVVP) and proposes a novel, uncertainty-aware algorithm called UWAV towards that end. UWAV works by producing more reliable segment‑level pseudo‑labels while explicitly weighting each label by its prediction uncertainty. This uncertainty‑aware training, combined with a feature‑mixup regularization scheme, promotes inter‑segment consistency in the pseudo-labels. As a result, UWAV achieves state‑of‑the‑art performance on two AVVP datasets across multiple metrics, demonstrating both effectiveness and strong generalizability.

        Paper: https://www.merl.com/publications/TR2025-072

        2. "TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection" by Y. G. Jung, J. Park, J. Yoon, K.-C. Peng, W. Kim, A. B. J. Teoh, and O. Camps.

        This work tackles unsupervised anomaly detection in complex scenarios where normal data is noisy and has an unknown, imbalanced class distribution. Existing models face a trade-off between robustness to noise and performance on rare (tail) classes. To address this, the authors propose TailSampler, which estimates class sizes from embedding similarities to isolate tail samples. Using TailSampler, they develop TailedCore, a memory-based model that effectively captures tail class features while remaining noise-robust, outperforming state-of-the-art methods in extensive evaluations.

        paper: https://www.merl.com/publications/TR2025-077


        MERL Co-Organized Workshops:

        1. Multimodal Algorithmic Reasoning (MAR) Workshop, organized by A. Cherian, K.-C. Peng, S. Lohit, H. Zhou, K. Smith, L. Xue, T. K. Marks, and J. Tenenbaum.

        Workshop link: https://marworkshop.github.io/cvpr25/

        2. The 6th Workshop on Fair, Data-Efficient, and Trusted Computer Vision, organized by N. Ratha, S. Karanam, Z. Wu, M. Vatsa, R. Singh, K.-C. Peng, M. Merler, and K. Varshney.

        Workshop link: https://fadetrcv.github.io/2025/


        Workshop Papers:

        1. "FreBIS: Frequency-Based Stratification for Neural Implicit Surface Representations" by N. Sawada, P. Miraldo, S. Lohit, T.K. Marks, and M. Chatterjee (Oral)

        With their ability to model object surfaces in a scene as a continuous function, neural implicit surface reconstruction methods have made remarkable strides recently, especially over classical 3D surface reconstruction methods, such as those that use voxels or point clouds. Towards this end, we propose FreBIS - a neural implicit‑surface framework that avoids overloading a single encoder with every surface detail. It divides a scene into several frequency bands and assigns a dedicated encoder (or group of encoders) to each band, then enforces complementary feature learning through a redundancy‑aware weighting module. Swapping this frequency‑stratified stack into an off‑the‑shelf reconstruction pipeline markedly boosts 3D surface accuracy and view‑consistent rendering on the challenging BlendedMVS dataset.

        paper: https://www.merl.com/publications/TR2025-074

        2. "Multimodal 3D Object Detection on Unseen Domains" by D. Hegde, S. Lohit, K.-C. Peng, M. J. Jones, and V. M. Patel.

        LiDAR-based object detection models often suffer performance drops when deployed in unseen environments due to biases in data properties like point density and object size. Unlike domain adaptation methods that rely on access to target data, this work tackles the more realistic setting of domain generalization without test-time samples. We propose CLIX3D, a multimodal framework that uses both LiDAR and image data along with supervised contrastive learning to align same-class features across domains and improve robustness. CLIX3D achieves state-of-the-art performance across various domain shifts in 3D object detection.

        paper: https://www.merl.com/publications/TR2025-078

        3. "Improving Open-World Object Localization by Discovering Background" by A. Singh, M. J. Jones, K.-C. Peng, M. Chatterjee, A. Cherian, and E. Learned-Miller.

        This work tackles open-world object localization, aiming to detect both seen and unseen object classes using limited labeled training data. While prior methods focus on object characterization, this approach introduces background information to improve objectness learning. The proposed framework identifies low-information, non-discriminative image regions as background and trains the model to avoid generating object proposals there. Experiments on standard benchmarks show that this method significantly outperforms previous state-of-the-art approaches.

        paper: https://www.merl.com/publications/TR2025-058

        4. "PF3Det: A Prompted Foundation Feature Assisted Visual LiDAR 3D Detector" by K. Li, T. Zhang, K.-C. Peng, and G. Wang.

        This work addresses challenges in 3D object detection for autonomous driving by improving the fusion of LiDAR and camera data, which is often hindered by domain gaps and limited labeled data. Leveraging advances in foundation models and prompt engineering, the authors propose PF3Det, a multi-modal detector that uses foundation model encoders and soft prompts to enhance feature fusion. PF3Det achieves strong performance even with limited training data. It sets new state-of-the-art results on the nuScenes dataset, improving NDS by 1.19% and mAP by 2.42%.

        paper: https://www.merl.com/publications/TR2025-076

        5. "Noise Consistency Regularization for Improved Subject-Driven Image Synthesis" by Y. Ni., S. Wen, P. Konius, A. Cherian

        Fine-tuning Stable Diffusion enables subject-driven image synthesis by adapting the model to generate images containing specific subjects. However, existing fine-tuning methods suffer from two key issues: underfitting, where the model fails to reliably capture subject identity, and overfitting, where it memorizes the subject image and reduces background diversity. To address these challenges, two auxiliary consistency losses are porposed for diffusion fine-tuning. First, a prior consistency regularization loss ensures that the predicted diffusion noise for prior (non- subject) images remains consistent with that of the pretrained model, improving fidelity. Second, a subject consistency regularization loss enhances the fine-tuned model’s robustness to multiplicative noise modulated latent code, helping to preserve subject identity while improving diversity. Our experimental results demonstrate the effectiveness of our approach in terms of image diversity, outperforming DreamBooth in terms of CLIP scores, background variation, and overall visual quality.

        paper: https://www.merl.com/publications/TR2025-073

        6. "LatentLLM: Attention-Aware Joint Tensor Compression" by T. Koike-Akino, X. Chen, J. Liu, Y. Wang, P. Wang, M. Brand

        We propose a new framework to convert a large foundation model such as large language models (LLMs)/large multi- modal models (LMMs) into a reduced-dimension latent structure. Our method uses a global attention-aware joint tensor decomposition to significantly improve the model efficiency. We show the benefit on several benchmark including multi-modal reasoning tasks.

        paper: https://www.merl.com/publications/TR2025-075

        7. "TuneComp: Joint Fine-Tuning and Compression for Large Foundation Models" by T. Koike-Akino, X. Chen, J. Liu, Y. Wang, P. Wang, M. Brand

        To reduce model size during post-training, compression methods, including knowledge distillation, low-rank approximation, and pruning, are often applied after fine- tuning the model. However, sequential fine-tuning and compression sacrifices performance, while creating a larger than necessary model as an intermediate step. In this work, we aim to reduce this gap, by directly constructing a smaller model while guided by the downstream task. We propose to jointly fine-tune and compress the model by gradually distilling it to a pruned low-rank structure. Experiments demonstrate that joint fine-tuning and compression significantly outperforms other sequential compression methods.

        paper: https://www.merl.com/publications/TR2025-079
    •  
    •  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
    •  

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  • Awards

    •  AWARD    CVPR 2011 Longuet-Higgins Prize
      Date: June 25, 2011
      Awarded to: Paul A. Viola and Michael J. Jones
      Awarded for: "Rapid Object Detection using a Boosted Cascade of Simple Features"
      Awarded by: Conference on Computer Vision and Pattern Recognition (CVPR)
      MERL Contact: Michael J. Jones
      Research Area: Machine Learning
      Brief
      • Paper from 10 years ago with the largest impact on the field: "Rapid Object Detection using a Boosted Cascade of Simple Features", originally published at Conference on Computer Vision and Pattern Recognition (CVPR 2001).
    •  
    See All Awards for MERL

  • Research Highlights

  • MERL Publications

    •  Lai, Y.-H., Ebbers, J., Wang, Y.-C.F., Germain, F.G., Jones, M.J., Chatterjee, M., "UWAV: Uncertainty-weighted Weakly-supervised Audio-Visual Video Parsing", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2025.
      BibTeX TR2025-072 PDF
      • @inproceedings{Lai2025jun,
      • author = {Lai, Yung-Hsuan and Ebbers, Janek and Wang, Yu-Chiang Frank and Germain, François G and Jones, Michael J. and Chatterjee, Moitreya},
      • title = {{UWAV: Uncertainty-weighted Weakly-supervised Audio-Visual Video Parsing}},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2025,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2025-072}
      • }
    •  Hegde, D., Lohit, S., Peng, K.-C., Jones, M.J., Patel, V.M., "Multimodal 3D Object Detection on Unseen Domains", IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshop, June 2025.
      BibTeX TR2025-078 PDF
      • @inproceedings{Hegde2025jun,
      • 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}},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshop},
      • year = 2025,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2025-078}
      • }
    •  Singh, A., Jones, M.J., Peng, K.-C., Chatterjee, M., Cherian, A., Learned-Miller, E., "Improving Open-World Object Localization by Discovering Background", CVPR Workshop on Domain Generalization: Evolution, Breakthroughs and Future Horizon, May 2025.
      BibTeX TR2025-058 PDF
      • @inproceedings{Singh2025may,
      • author = {Singh, Ashish and Jones, Michael J. and Peng, Kuan-Chuan and Chatterjee, Moitreya and Cherian, Anoop and Learned-Miller, Erik},
      • title = {{Improving Open-World Object Localization by Discovering Background}},
      • booktitle = {CVPR Workshop on Domain Generalization: Evolution, Breakthroughs and Future Horizon},
      • year = 2025,
      • month = may,
      • url = {https://www.merl.com/publications/TR2025-058}
      • }
    •  Tang, H., Ellis, K., Lohit, S., Jones, M.J., Chatterjee, M., "Programmatic Video Prediction Using Large Language Models", International Conference on Learning Representations Workshops (ICLRW), April 2025.
      BibTeX TR2025-049 PDF
      • @inproceedings{Tang2025apr,
      • author = {Tang, Hao and Ellis, Kevin and Lohit, Suhas and Jones, Michael J. and Chatterjee, Moitreya},
      • title = {{Programmatic Video Prediction Using Large Language Models}},
      • booktitle = {International Conference on Learning Representations Workshops (ICLRW)},
      • year = 2025,
      • month = apr,
      • url = {https://www.merl.com/publications/TR2025-049}
      • }
    •  Mumcu, F., Jones, M.J., Yilmaz, Y., Cherian, A., "ComplexVAD: Detecting Interaction Anomalies in Video", IEEE Winter Conference on Applications of Computer Vision (WACV) Workshop, February 2025.
      BibTeX TR2025-016 PDF Data
      • @inproceedings{Mumcu2025feb,
      • author = {Mumcu, Furkan and Jones, Michael J. and Yilmaz, Yasin and Cherian, Anoop},
      • title = {{ComplexVAD: Detecting Interaction Anomalies in Video}},
      • booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV) Workshop},
      • year = 2025,
      • month = feb,
      • url = {https://www.merl.com/publications/TR2025-016}
      • }
    See All MERL Publications for Mike

  • Other Publications

    •  G.B. Huang, M.J. Jones, E. Learned-Miller and others, "Lfw results using a combined nowak plus merl recognizer", 2008.
      BibTeX
      • @Article{huang2008lfw,
      • author = {Huang, G.B. and Jones, M.J. and Learned-Miller, E. and others},
      • title = {Lfw results using a combined nowak plus merl recognizer},
      • year = 2008
      • }
    •  T.J. Cham, S. Krishnamoorthy and M. Jones, "Analogous view transfer for gaze correction in video sequences", Control, Automation, Robotics and Vision, 2002. ICARCV 2002. 7th International Conference on, 2002, vol. 3, pp. 1415-1420.
      BibTeX
      • @Inproceedings{cham2002analogous,
      • author = {Cham, T.J. and Krishnamoorthy, S. and Jones, M.},
      • title = {Analogous view transfer for gaze correction in video sequences},
      • booktitle = {Control, Automation, Robotics and Vision, 2002. ICARCV 2002. 7th International Conference on},
      • year = 2002,
      • volume = 3,
      • pages = {1415--1420},
      • organization = {IEEE}
      • }
    •  M.J. Jones and J.M. Rehg, "Statistical color models with application to skin detection", International Journal of Computer Vision, Vol. 46, No. 1, pp. 81-96, 2002.
      BibTeX
      • @Article{jones2002statistical,
      • author = {Jones, M.J. and Rehg, J.M.},
      • title = {Statistical color models with application to skin detection},
      • journal = {International Journal of Computer Vision},
      • year = 2002,
      • volume = 46,
      • number = 1,
      • pages = {81--96},
      • publisher = {Springer}
      • }
    •  S.B. Kang and M. Jones, "Appearance-based structure from motion using linear classes of 3-d models", International Journal of Computer Vision, Vol. 49, No. 1, pp. 5-22, 2002.
      BibTeX
      • @Article{kang2002appearance,
      • author = {Kang, S.B. and Jones, M.},
      • title = {Appearance-based structure from motion using linear classes of 3-d models},
      • journal = {International Journal of Computer Vision},
      • year = 2002,
      • volume = 49,
      • number = 1,
      • pages = {5--22},
      • publisher = {Springer}
      • }
    •  C.M. Procopiuc, M. Jones, P.K. Agarwal and TM Murali, "A Monte Carlo algorithm for fast projective clustering", Proceedings of the 2002 ACM SIGMOD international conference on Management of data, 2002, pp. 418-427.
      BibTeX
      • @Inproceedings{procopiuc2002monte,
      • author = {Procopiuc, C.M. and Jones, M. and Agarwal, P.K. and Murali, TM},
      • title = {A Monte Carlo algorithm for fast projective clustering},
      • booktitle = {Proceedings of the 2002 ACM SIGMOD international conference on Management of data},
      • year = 2002,
      • pages = {418--427},
      • organization = {ACM}
      • }
    •  P. Viola and M. Jones, "Fast and robust classification using asymmetric adaboost and a detector cascade", Proc. of NIPS01, 2001.
      BibTeX
      • @Article{viola2001fast,
      • author = {Viola, P. and Jones, M.},
      • title = {Fast and robust classification using asymmetric adaboost and a detector cascade},
      • journal = {Proc. of NIPS01},
      • year = 2001
      • }
    •  M.J. Jones and J.M. Rehg, "Statistical color models with application to skin detection", Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on., 1999, vol. 1.
      BibTeX
      • @Inproceedings{jones1999statistical,
      • author = {Jones, M.J. and Rehg, J.M.},
      • title = {Statistical color models with application to skin detection},
      • booktitle = {Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on.},
      • year = 1999,
      • volume = 1,
      • organization = {IEEE}
      • }
    •  T.D. Rikert, M.J. Jones and P. Viola, "A cluster-based statistical model for object detection", Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on, 1999, vol. 2, pp. 1046-1053.
      BibTeX
      • @Inproceedings{rikert1999cluster,
      • author = {Rikert, T.D. and Jones, M.J. and Viola, P.},
      • title = {A cluster-based statistical model for object detection},
      • booktitle = {Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on},
      • year = 1999,
      • volume = 2,
      • pages = {1046--1053},
      • organization = {IEEE}
      • }
    •  M.J. Jones and T. Poggio, "Hierarchical morphable models", Computer Vision and Pattern Recognition, 1998. Proceedings. 1998 IEEE Computer Society Conference on, 1998, pp. 820-826.
      BibTeX
      • @Inproceedings{jones1998hierarchical,
      • author = {Jones, M.J. and Poggio, T.},
      • title = {Hierarchical morphable models},
      • booktitle = {Computer Vision and Pattern Recognition, 1998. Proceedings. 1998 IEEE Computer Society Conference on},
      • year = 1998,
      • pages = {820--826},
      • organization = {IEEE}
      • }
    •  M.J. Jones and T. Poggio, "Multidimensional morphable models: A framework for representing and matching object classes", International Journal of Computer Vision, Vol. 29, No. 2, pp. 107-131, 1998.
      BibTeX
      • @Article{jones1998multidimensional,
      • author = {Jones, M.J. and Poggio, T.},
      • title = {Multidimensional morphable models: A framework for representing and matching object classes},
      • journal = {International Journal of Computer Vision},
      • year = 1998,
      • volume = 29,
      • number = 2,
      • pages = {107--131},
      • publisher = {Springer}
      • }
    •  T.D. Rikert and M.J. Jones, "Gaze estimation using morphable models", Automatic Face and Gesture Recognition, 1998. Proceedings. Third IEEE International Conference on, 1998, pp. 436-441.
      BibTeX
      • @Inproceedings{rikert1998gaze,
      • author = {Rikert, T.D. and Jones, M.J.},
      • title = {Gaze estimation using morphable models},
      • booktitle = {Automatic Face and Gesture Recognition, 1998. Proceedings. Third IEEE International Conference on},
      • year = 1998,
      • pages = {436--441},
      • organization = {IEEE}
      • }
    •  M.J. Jones, "Multidimensional morphable models: A framework for representing and matching object classes", 1997.
      BibTeX
      • @Phdthesis{jones1997multidimensional,
      • author = {Jones, M.J.},
      • title = {Multidimensional morphable models: A framework for representing and matching object classes},
      • year = 1997,
      • publisher = {Massachusetts Institute of Technology}
      • }
    •  M.J. Jones, P. Sinha, T. Vetter and T. Poggio, "Top-down learning of low-level vision tasks", Current Biology, Vol. 7, No. 12, pp. 991-994, 1997.
      BibTeX
      • @Article{jones1997top,
      • author = {Jones, M.J. and Sinha, P. and Vetter, T. and Poggio, T.},
      • title = {Top-down learning of low-level vision tasks},
      • journal = {Current Biology},
      • year = 1997,
      • volume = 7,
      • number = 12,
      • pages = {991--994},
      • publisher = {Elsevier}
      • }
    •  T. Vetter, M.J. Jones and T. Poggio, "A bootstrapping algorithm for learning linear models of object classes", Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on, 1997, pp. 40-46.
      BibTeX
      • @Inproceedings{vetter1997bootstrapping,
      • author = {Vetter, T. and Jones, M.J. and Poggio, T.},
      • title = {A bootstrapping algorithm for learning linear models of object classes},
      • booktitle = {Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on},
      • year = 1997,
      • pages = {40--46},
      • organization = {IEEE}
      • }
    •  F. Girosi, M. Jones and T. Poggio, "Regularization theory and neural networks architectures", Neural computation, Vol. 7, No. 2, pp. 219-269, 1995.
      BibTeX
      • @Article{girosi1995regularization,
      • author = {Girosi, F. and Jones, M. and Poggio, T.},
      • title = {Regularization theory and neural networks architectures},
      • journal = {Neural computation},
      • year = 1995,
      • volume = 7,
      • number = 2,
      • pages = {219--269},
      • publisher = {MIT Press}
      • }
    •  M.J. Jones and T. Poggio, "Model-based matching of line drawings by linear combinations of prototypes", Computer Vision, 1995. Proceedings., Fifth International Conference on, 1995, pp. 531-536.
      BibTeX
      • @Inproceedings{jones1995model,
      • author = {Jones, M.J. and Poggio, T.},
      • title = {Model-based matching of line drawings by linear combinations of prototypes},
      • booktitle = {Computer Vision, 1995. Proceedings., Fifth International Conference on},
      • year = 1995,
      • pages = {531--536},
      • organization = {IEEE}
      • }
    •  F. Girosi, M. Jones and T. Poggio, "Priors stabilizers and basis functions: From regularization to radial, tensor and additive splines", MIT AI Lab Memo 1430, 1993.
      BibTeX
      • @Article{girosi1993priors,
      • author = {Girosi, F. and Jones, M. and Poggio, T.},
      • title = {Priors stabilizers and basis functions: From regularization to radial, tensor and additive splines},
      • journal = {MIT AI Lab Memo 1430},
      • year = 1993
      • }
    •  T. Poggio, F. Girosi and M. Jones, "From regularization to radial, tensor and additive splines", Neural Networks, 1993. IJCNN'93-Nagoya. Proceedings of 1993 International Joint Conference on, 1993, vol. 1, pp. 223-227.
      BibTeX
      • @Inproceedings{poggio1993regularization,
      • author = {Poggio, T. and Girosi, F. and Jones, M.},
      • title = {From regularization to radial, tensor and additive splines},
      • booktitle = {Neural Networks, 1993. IJCNN'93-Nagoya. Proceedings of 1993 International Joint Conference on},
      • year = 1993,
      • volume = 1,
      • pages = {223--227},
      • organization = {IEEE}
      • }
    •  M.J. Jones, "Using recurrent networks for dimensionality reduction", 1992, Massachusetts Institute of Technology.
      BibTeX
      • @Mastersthesis{jones1992using,
      • author = {Jones, M.J.},
      • title = {Using recurrent networks for dimensionality reduction},
      • school = {Massachusetts Institute of Technology},
      • year = 1992
      • }

  • Software & Data Downloads

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  • MERL Issued Patents

    • Title: "System and Method for Anomaly Detection of a Scene"
      Inventors: Jones, Michael J.
      Patent No.: 12,106,562
      Issue Date: Oct 1, 2024
    • Title: "System and Method for Detecting Objects in Video Sequences"
      Inventors: Jones, Michael J.; Broad, Alexander
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      Issue Date: Nov 2, 2021
    • Title: "System and Method for Detecting Motion Anomalies in Video"
      Inventors: Jones, Michael J.
      Patent No.: 10,970,823
      Issue Date: Apr 6, 2021
    • Title: "System and Method for Detecting Anomalies in Video using a Similarity Function Trained by Machine Learning"
      Inventors: Jones, Michael J.
      Patent No.: 10,824,935
      Issue Date: Nov 3, 2020
    • 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: "System and Method for Image Comparison Based on Hyperplanes Similarity"
      Inventors: Jones, Michael J.
      Patent No.: 10,452,958
      Issue Date: Oct 22, 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 Anomaly Detection in Time Series Data Based on Spectral Partitioning"
      Inventors: Nikovski, Daniel N.; Kniazev, Andrei; Jones, Michael J.
      Patent No.: 9,984,334
      Issue Date: May 29, 2018
    • Title: "Method for Learning Exemplars for Anomaly Detection"
      Inventors: Jones, Michael J.; Nikovski, Daniel N.
      Patent No.: 9,779,361
      Issue Date: Oct 3, 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 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 for Detecting Anomalies in a Time Series Data with Trajectory and Stochastic Components"
      Inventors: Jones, Michael J.
      Patent No.: 9,146,800
      Issue Date: Sep 29, 2015
    • Title: "Method for Predicting Future Travel Time Using Geospatial Inference"
      Inventors: Jones, Michael J.; Nikovski, Daniel N.; Geng, Yanfeng
      Patent No.: 9,122,987
      Issue Date: Sep 1, 2015
    • Title: "Method for Detecting Anomalies in Multivariate Time Series Data"
      Inventors: Nikovski, Daniel N.; Jones, Michael J.
      Patent No.: 9,075,713
      Issue Date: Jul 7, 2015
    • Title: "Camera-Based 3D Climate Control"
      Inventors: Marks, Tim; Jones, Michael J.
      Patent No.: 8,929,592
      Issue Date: Jan 6, 2015
    • Title: "Object Detection in Depth Images"
      Inventors: Jones, Michael J.; Tuzel, C. Oncel; Si, Weiguang
      Patent No.: 8,406,470
      Issue Date: Mar 26, 2013
    • Title: "Method for identifying Faces in Images with Improved Accuracy Using Compressed Feature Vectors"
      Inventors: Thornton, Jay E.; Jones, Michael J.
      Patent No.: 8,213,691
      Issue Date: Jul 3, 2012
    • Title: "Method for Synthetically Images of Objects"
      Inventors: Jones, Michael J.; Marks, Tim; Kumar, Ritwik
      Patent No.: 8,194,072
      Issue Date: Jun 5, 2012
    • Title: "Method for Localizing Irises in Images Using Gradients and Textures"
      Inventors: Jones, Michael J.; Guo, Guo Dong
      Patent No.: 7,583,823
      Issue Date: Sep 1, 2009
    • Title: "Detecting Pedestrians Using Patterns of Motion and Appearance in Videos"
      Inventors: Jones, Michael J.; Viola, Paul A.
      Patent No.: 7,212,651
      Issue Date: May 1, 2007
    • Title: "Detecting Arbitrarily Oriented Objects in Images"
      Inventors: Viola, Paul A.; Jones, Michael J.
      Patent No.: 7,197,186
      Issue Date: Mar 27, 2007
    • Title: "Object Recognition System"
      Inventors: Viola, Paul A.; Jones, Michael J.
      Patent No.: 7,031,499
      Issue Date: Apr 18, 2006
    • Title: "System and Method for Detecting Objects in Images"
      Inventors: Viola, Paul A.; Jones, Michael J.
      Patent No.: 7,020,337
      Issue Date: Mar 28, 2006
    See All Patents for MERL