Erik Learned-Miller Erik G. Learned-Miller
Professor and Chair of the Faculty
The Manning College of Information and Computer Sciences
University of Massachusetts, Amherst

140 Governors Drive, Office 200
Amherst, MA 01003

E-mail: elm at cs.umass.edu
Computer Vision Lab

News:

  • We have started work on a New Building! Check out the College web pages for architectural drawings and renderings.
  • On September 1, 2022, I started my new position as Chair of the Faculty in the Manning College of Information and Computer Sciences. I continue to co-direct the Computer Vision Lab with Subhransu Maji.

Home Publications Students Teaching Funding Code FAQ

By Date

By Area

By Keyword

By Type

By Publication Locale

Publications by Locale

Vision and Learning Journals: PAMI | IJCV | CVIU | MVAP | JMLR | IEEE IT | IJDAR | Neural Networks
Vision Conferences: CVPR | ICCV | ECCV | BMVC | WACV | SGP | FandG | ICPR
Probability and Statistics Journals: Statistics and Probability Letters
Learning Conferences: NIPS | ICML | UAI | AAAI | ECML | ICA
Information Retrieval: SIGIR | CIKM
Robotics Conferences: IROS | ICRA | ISRR | Humanoids | RSS
Medical Conferences: MICCAI | IPMI
Document Conferences: ICDAR
Signal Processing Conferences: ICASSP
Systems Journals: MOBILE COMPUTING
Systems Conferences: USENIX FAST | MOBICOM | ACM EMBEDDED COMP. SYS. | USENIX SECURITY | PETS
Medical and Bio Journals: Medical Image Analysis | Clin. Endo. | BMC Bio. | Scientific Reports | J.Neuro. | JCAT
Other: Workshops | Tech Reps.
IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI)
International Journal of Computer Vision (IJCV)
Computer Vision and Image Understanding (CVIU)
Machine Vision and Applications (MVAP)
Journal of Machine Learning Research (JMLR)
IEEE Transactions on Information Theory
International Journal on Document Analysis and Recognition (IJDAR)
Neural Networks
IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • Zitian Chen, Yikang Shen, Mingyu Ding, Zhenfang Chen, Hengshuang Zhao, Erik Learned-Miller and Chuang Gan.
    Mod-Squad: Designing mixtures of experts as modular multi-task learners.
    Computer Vision and Pattern Recognition (CVPR), 10 pages, 2023.
    [pdf]

  • Ashish Singh, Michael Jones, and Erik Learned-Miller.
    EVAL: Explainable Video Anomaly Localization.
    Computer Vision and Pattern Recognition (CVPR), 10 pages, 2023.
    [pdf]

  • Huaizu Jiang, Ishan Misra, Marcus Rohrbach, Erik Learned-Miller, and Xinlei Chen.
    In Defense of grid features for visual question answering.
    Computer Vision and Pattern Recognition (CVPR), 10 pages, 2020.
    [pdf]

  • Aruni RoyChowdhury, Prithvijit Chakrabarty, Ashish Singh, SouYoung Jin, Huaizu Jiang, Liangliang Cao, and Erik Learned-Miller.
    Automatic adaptation of object detectors to new domains using self-training.
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 11 pages, 2019.
    [pdf]

  • Hang Su, Varun Jampani, Deqing Sun, Orazio Gallo, Erik Learned-Miller, and Jan Kautz.
    Pixel Adaptive Convolutional Neural Networks.
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 10 pages, 2019.
    [pdf]

  • Pia Bideau, Aruni RoyChowdhury, Rakesh Menon, and Erik Learned-Miller.
    The best of both worlds: Combining CNNs and geometric constraints for hierarchichal motion segmentation.
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 10 pages, 2018.
    [pdf]

  • Huaizu Jiang, Deqing Sun, Varun Jampani, Ming-Hsuan Yang, Erik Learned-Miller, Jan Kautz.
    Super SloMo: High quality estimation of multiple intermediate frames for video interpolation.
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 12 pages, 2018.
    [pdf]

  • Andrew Kae, Benjamin Marlin, and Erik Learned-Miller.
    The shape-time random field for semantic video labeling.
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.
    [pdf]

  • Andrew Kae*, Kihyuk Sohn*, Honglak Lee and Erik Learned-Miller.
    Augmenting CRFs with Boltzmann machine shape priors for image labeling.
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013.
    *The first and second authors made equal contributions and should be considered co-first authors.
    [pdf]

  • Gary B. Huang, Honglak Lee, and Erik Learned-Miller.
    Learning hierarchical representations for face verification.
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
    [pdf]

  • Laura Sevilla Lara and Erik Learned-Miller.
    Distribution fields for tracking.
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
    [pdf]

  • Manjunath Narayana, Allen Hanson, and Erik Learned-Miller.
    Background modeling using adaptive pixelwise kernel variances in a hybrid feature space.
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
    [pdf]

  • Vidit Jain and Erik Learned-Miller.
    Online domain-adaptation of a pre-trained cascade of classifiers.
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.
    [pdf]

  • David L. Smith, Jacqueline Feild, and Erik Learned-Miller.
    Enforcing similarity constraints with integer programming for better scene text recognition.
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.
    [pdf]

  • Andrew Kae, Gary B. Huang, Carl Doersch, and Erik Learned-Miller.
    Improving state-of-the-art OCR through high-precision document-specific modeling.
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010.
    [pdf]

  • Jerod Weinman and Erik Learned-Miller.
    Improving recognition of novel input with similarity.
    In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Volume 1, pp. 308-315, 2006.
    [pdf]

  • Tamara Berg, Alex Berg, Jaety Edwards, Michael Maire, Ryan White, Yee Whye Teh, Erik Learned-Miller and David Forsyth.
    Names and faces in the news.
    In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 2, pp. 848-854, 2004.
    [pdf]

  • Erik Miller and Christophe Chefd'hotel.
    Practical non-parametric density estimation on a transformation group for vision.
    In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Volume 2, pp. 114-121, 2003.
    [pdf]

  • Erik Miller, Nick Matsakis, and Paul Viola.
    Learning from one example through shared densities on transforms.
    Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Volume 1, pp. 464-471, 2000.
    [pdf]

International Conference on Computer Vision (ICCV)
  • Fabien Delattre, David Dirnfeld, Phat Nguyen, Stephen Scarano, Michael J. Jones, Pedro Miraldo, Erik Learned-Miller.
    Robust frame-to-frame camera rotation estimation in crowded scenes.
    International Conference on Computer Vision (ICCV), 10 pages, 2023.
    [pdf] [Project page]

  • Cheng Gu, Erik Learned-Miller, Daniel Sheldon, Guillermo Gallego, and Pia Bideau.
    The spatio-temporal Poisson point process: A simple model for the alignment of event camera data.
    International Conference on Computer Vision (ICCV), 10 pages, 2021.
    [pdf]

  • Huaizu Jiang, Deqing Sun, Varun Jampani, Zhaoyang Lv, Erik Learned-Miller, and Jan Kautz.
    SENSE: A shared encoder network for scene-flow estimation.
    International Conference on Computer Vision (ICCV), 10 pages, 2019.
    [pdf]

  • SouYoung Jin, Hang Su, Chris Stauffer, and Erik Learned-Miller.
    End-to-end face detection and cast grouping in movies using Erdos-Renyi clustering.
    International Conference on Computer Vision (ICCV), 10 pages, 2017.
    [pdf] [Project page]

  • Hang Su, Subhransu Maji, Evangelos Kalogerakis, and Erik Learned-Miller.
    Multi-view convolutional neural networks for 3D shape recognition.
    International Conference on Computer Vision (ICCV), 9 pages, 2015.
    [project] [pdf]

  • Manjunath Narayana, Allen Hanson, and Erik Learned-Miller.
    Coherent motion segmentation in moving camera videos using optical flow orientations.
    International Conference on Computer Vision (ICCV), 2013.
    [pdf] [Supplementary Material]

  • Gary B. Huang, Vidit Jain, and Erik Learned-Miller.
    Unsupervised joint alignment of complex images.
    International Conference on Computer Vision (ICCV), 2007.
    [pdf]

  • Vidit Jain, Erik Learned-Miller, and Andrew McCallum.
    People-LDA: Anchoring topics to people using face recognition.
    International Conference on Computer Vision (ICCV), 2007.
    [pdf]

  • Andras Ferencz, Erik Learned-Miller, and Jitendra Malik.
    Building a classification cascade for visual identification from one example.
    In International Conference on Computer Vision (ICCV), pp. 286-293, 2005.
    [pdf]

  • Erik Miller and Kinh Tieu.
    Color eigenflows: Statistical modeling of joint color changes.
    International Conference on Computer Vision (ICCV), Volume 1, pp. 607-614, 2001.
    [pdf]

European Conference on Computer Vision (ECCV)
British Machine Vision Conference (BMVC)
IEEE Winter Conference on Applications of Computer Vision (WACV)
Eurographics Symposium on Geometry Processing (SGP)
IEEE Conference on Automatic Face and Gesture Recognition (FandG)
International Conference on Pattern Recognition (ICPR)
Statistics and Probability Letters (SPL)
Neural Information Processing Systems (NIPS)
  • Yash Chandak, Scott Niekum, Bruno Castro da Silva, Erik Learned-Miller, Emma Brunskill, and Philip S. Thomas.
    Universal off-policy evaluation.
    In Neural Information Processing Systems (NeurIPS) , 2021.
    [arXiv]

  • Haw-Shiuan Chang, Erik Learned-Miller, and Andrew McCallum.
    Active bias: Training a more accurate neural network by emphasizing high variance samples.
    In Neural Information Processing Systems (NIPS) , 2017.
    [arXiv]

  • Gary B. Huang, Marwan Mattar, Honglak Lee, and Erik Learned-Miller.
    Learning to align from scratch.
    In Neural Information Processing Systems (NIPS) , 2012.
    [pdf]

  • Andras Ferencz, Erik Learned-Miller and Jitendra Malik.
    Learning hyper-features for visual identification.
    In Neural Information Processing Systems (NIPS) 17, pp. 425-432, 2005.
    [pdf]

  • Erik Learned-Miller and Parvez Ahammad.
    Joint MRI bias removal using entropy minimization across images.
    In Neural Information Processing Systems (NIPS) 17, pp. 761-768, 2005.
    [pdf]

  • Kinh Tieu and Erik Miller.
    Unsupervised color constancy.
    In Neural Information Processing Systems (NIPS) 15, pp. 1303-1310, 2003.
    [pdf]

  • Chris Stauffer, Erik Miller and Kinh Tieu.
    Transform-invariant image decomposition with similarity templates.
    In Neural Information Processing Systems (NIPS) 14, pp. 1295-1302, 2002.
    [pdf]

International Conference on Machine Learning (ICML)
Uncertainty in Artificial Intelligence (UAI)
Conference of the American Association of Artificial Intelligence (AAAI)
International Conference on Document Analysis and Recognition (ICDAR)
  • Jacqueline Feild, Erik Learned-Miller and David A. Smith.
    Using a probabilistic syllable model to improve scene text recognition.
    Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), 2013.
    [pdf]

  • Jacqueline Feild and Erik Learned-Miller.
    Improving open-vocabulary scene text recognition.
    Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), 2013.
    [pdf]

  • Yahan Zhou, Jacqueline Feild, Rui Wang, and Erik Learned-Miller.
    Scene text segmentation via inverse rendering.
    Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), 2013.
    [pdf]

  • Andrew Kae and Erik Learned-Miller.
    Learning on the fly: Font free approaches to difficult OCR problems.
    Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), 2009.
    [pdf]

  • Michael Wick, Michael G. Ross and Erik Learned-Miller.
    Context-sensitive error correction: Using topic models to improve OCR.
    Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), 2007.
    [pdf]

  • Jerod Weinman, Erik Learned-Miller, and Allen Hanson.
    Fast lexicon-based scene text recognition with sparse belief propagation.
    Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), 2007.
    [pdf]

  • Gary C. Huang, Erik Learned-Miller, and Andrew McCallum.
    Cryptogram decoding for OCR using numerization strings.
    Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), 2007.
    [pdf]

International Conference on Acoustics, Speech and Signal Processing (ICASSP)
European Conference on Machine Learning (ECML)
International Conference on Independent Components Analysis (ICA)
Special Interest Group on Information Retrieval (SIGIR)
Conference on Information and Knowledge Management (CIKM)
International Conference on Intelligent Robots and Systems (IROS)
International Conference on Robotics and Automation (ICRA)
International Symposium of Robotics Research (ISRR)
International Conference on Humanoid Robots
Robotics: Science and Systems (RSS)
Medical Image Computing and Computer-Assisted Intervention (MICCAI)
Information Processing in Medical Imaging (IPMI)
IEEE Mobile Computing
USENIX Conference on File and Storage Technologies (FAST)
MOBICOM
ACM Embedded Computing Systems
USENIX Security Symposium
Privacy Enhancing Technologies Symposium
Scientific Reports
Medical Image Analysis
BMC (BioMed Central) Bioinformatics
Clinical Endocrinology
Journal of Neurosurgery (JNeuro)
  • Douglas Cohen, Jonathan Lustgarten, Erik Miller, Alexander Khandji and Robert Goodman.
    Effects of coregistration of MR to CT images on MR stereotactic accuracy.
    Journal of Neurosurgery, Volume 82, pp. 772-779, 1995.

Journal of Computer Assisted Tomography (JCAT)
  • Robert Malison, Erik Miller, Robin Greene, Greg McCarthy, Dennis Charney and Robert Innis.
    Computer assisted coregistration of multislice SPECT and MR brain images by fixed external fiducials.
    Journal of Computer Assisted Tomography, Volume 17, pp. 952-960, 1993.
Workshops (Vision and Medical)
  • Li Yang Ku, Shiraj Sen, Erik Learned-Miller and Rod Grupen.
    Action-based models for belief-space planning.
    In Workshop on Information-Based Grasp and Manipulation Planning, at Robotics: Science and Systems, 6 pages, July, 2014.
    [pdf]

  • Li Yang Ku, Shiraj Sen, Erik Learned-Miller and Rod Grupen.
    The aspect transition graph: An affordance-based model.
    In Second Workshop on Affordances: Visual Perception of Affordances and Functional Visual Primitives for Scene Analysis, at the European Conference on Computer Vision, 7 pages, September, 2014.
    [pdf]

  • Gary B. Huang, Manjunath Narayana, and Erik Learned-Miller.
    Towards unconstrained face recognition.
    In The Sixth IEEE Computer Society Workshop on Perceptual Organization in Computer Vision IEEE CVPR, 2008.
    [pdf]

  • Dov Katz, Emily Horrell, Yuandong Yang, Brendan Burns, Thomas Buckley, Anna Grishkan, Volodymyr Zhylkovskyy, Oliver Brock, and Erik Learned-Miller.
    The UMass mobile manipulator UMan: An experimental platform for autonomous mobile manipulation.
    In Workshop on Manipulation in Human Environments, at Robotics: Science and Systems, 2006.
    [pdf]

  • Manjunatha N. Jagalur, Chris Pal, Erik Learned-Miller, R. T. Zoeller and David Kulp.
    The processing and analysis of in situ gene expression images of the mouse brain.
    Workshop on New Problems and Methods in Computational Biology, at Neural Information Processing Systems, 2006.
    [pdf]

  • Lilla Zollei, Erik Learned-Miller, Eric Grimson, and William Wells.
    Efficient population registration of 3D data.
    In Workshop on Computer Vision for Biomedical Image Applications: Current Techniques and Future Trends, at the International Conference of Computer Vision (best paper award), 2005.
    [pdf]

  • Dimitri A. Lisin, Marwan A. Mattar, Matthew B. Blaschko, Mark C. Benfield, and Erik G. Learned-Miller.
    Combining local and global features for object class recognition.
    In Workshop on Learning in Computer Vision and Pattern Recognition at IEEE CVPR, 2005.
    [pdf]

  • Marwan A. Mattar, Allen R. Hanson, and Erik G. Learned-Miller.
    Sign classification using local and meta-features.
    In Proceedings of the IEEE Workshop on Computer Vision Applications for the Visually Impaired (in conjunction with CVPR), 2005.
    [pdf]

  • Erik Miller, Kinh Tieu and Eric Grimson.
    Lighting invariance through joint color change models.
    Proceedings of Workshop on Identifying Object Across Variations in Lighting: Psychophysics and Computation, at IEEE Conference on Computer Vision and Pattern Recognition, 2001.
    [pdf]

Technical Reports
  • Gary B. Huang and Erik Learned-Miller.
    Labeled Faces in the Wild: Updates and new reporting procedures.
    UMass Amherst Technical Report UM-CS-2014-003, 5 pages, 2014.
    [pdf]

  • Jacqueline Feild and Erik Learned-Miller.
    Scene text recognition with bilateral regression.
    UMass Amherst Technical Report UM-CS-2012-021, 15 pages, 2012.
    [pdf]

  • Laura Sevilla Lara and Erik Learned-Miller.
    Distribution Fields.
    UMass Amherst Technical Report UM-CS-2011-027, 18 pages, 2011.
    [pdf]

  • Vidit Jain and Erik Learned-Miller.
    FDDB: A benchmark for face detection in unconstrained settings.
    UMass Amherst Technical Report UM-CS-2010-009, 11 pages, 2010.
    [pdf]

  • Andrew Kae, Gary B. Huang, and Erik Learned-Miller.
    Bounding the probability of error for high precision recognition.
    UMass Amherst Technical Report UM-CS-2009-031, 12 pages, 2009.
    [pdf]

  • Gary B. Huang, Manu Ramesh, Tamara Berg and Erik Learned-Miller.
    Labeled Faces in the Wild: A database for studying face recognition in unconstrained environments.
    UMass Amherst Technical Report 07-49, 11 pages, 2007.
    [pdf]

  • Marwan Mattar and Erik Learned-Miller.
    Improved generative models for continuous image features through tree-structured non-parametric distributions.
    UMass Amherst Technical Report 06-57, 10 pages, 2006.
    [pdf]

  • Jerod J. Weinman, Allen Hanson and Erik Learned-Miller.
    Joint feature selection for object detection and recognition.
    UMass Amherst Technical Report 06-54, 8 pages, 2006.
    [pdf]

  • Gary Huang, Erik Learned-Miller and Andrew McCallum.
    Cryptogram decoding for optical character recognition.
    UMass Amherst Technical Report 06-45, 12 pages, 2006.
    [pdf]

  • Joseph DeStefano, Qifeng Lu, and Erik Learned-Miller.
    A probabilistic upper bound on differential entropy.
    UMass Amherst Technical Report 05-12, 2005.
    [pdf]

  • Marwan A. Mattar, Allen R. Hanson, and Erik G. Learned-Miller.
    Sign classification for the visually impaired.
    University of Massachusetts Technical Report 05-14, 2005.
    [pdf]

  • Qifeng Lu, Erik Learned-Miller, Angela Paisley, Peter Trainer, Volker Blanz, Katrin Dedden, and Ralph Miller.
    Detecting acromegaly: Screening for diseases with a morphable model.
    UMass Amherst Technical Report 05-37, 2005.
    [pdf]

  • Erik Learned-Miller.
    Hyperspacings and the estimation of information theoretic quantities.
    UMass Amherst Technical Report 04-104, 2004.
    [pdf]

  • Erik Miller and John W. Fisher, III.
    Independent components analysis by direct entropy minimization.
    UC Berkeley Technical Report CSD-03-1221, 25 pages, 2003.
    [pdf]

  • Erik Miller, Kinh Tieu and Chris Stauffer.
    Learning object-independent modes of variation with feature flow fields.
    Massachusetts Institute of Technology, AI-Memo: AIM-2001-021, 9 pages, 2001.
    [pdf]