World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
44
Citations
15595
World Ranking
7385
National Ranking
3219

Overview

Erik Learned-Miller is affiliated with the University of Massachusetts Amherst in the United States. Their research primarily spans the field of Computer Science, with a strong focus on subfields such as Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Computational Mechanics, and Geology.

The scientist's work covers a range of topics, including:

  • Domain Adaptation and Few-Shot Learning
  • Advanced Image and Video Retrieval Techniques
  • Advanced Vision and Imaging
  • Multimodal Machine Learning Applications
  • 3D Shape Modeling and Analysis
  • 3D Surveying and Cultural Heritage
  • Statistical Methods and Inference

Erik Learned-Miller has published extensively, with frequent contributions to venues such as:

  • arXiv (Cornell University)
  • Lecture Notes in Computer Science
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Medical Image Analysis
  • Neural Networks

Recent notable publications include:

  • "The Spatio-Temporal Poisson Point Process: A Simple Model for the Alignment of Event Camera Data" (2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV))
  • "Image registration: Maximum likelihood, minimum entropy and deep learning" (2020, Medical Image Analysis)
  • "In Defense of Grid Features for Visual Question Answering" (2020, arXiv (Cornell University))
  • "A domain-agnostic approach for characterization of lifelong learning systems" (2023, Neural Networks)
  • "Self-Supervised Learning to Guide Scientifically Relevant Categorization of Martian Terrain Images" (2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW))

Frequent collaborators in Erik Learned-Miller's research include Aruni RoyChowdhury, Subhransu Maji, Evangelos Kalogerakis, Pia Bideau, and Matheus Gadelha.

Best Publications

  • Multi-view Convolutional Neural Networks for 3D Shape Recognition

    Hang Su;Subhransu Maji;Evangelos Kalogerakis;Erik Learned-Miller

  • FDDB: A benchmark for face detection in unconstrained settings

    Vidit Jain;Erik G Learned-Miller

  • Face Detection with the Faster R-CNN

    Huaizu Jiang;Erik Learned-Miller

  • Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation

    Huaizu Jiang;Deqing Sun;Varan Jampani;Ming-Hsuan Yang

  • Distribution fields for tracking

    Laura Sevilla-Lara;Erik Learned-Miller

  • Labeled Faces in the Wild: A Survey

    Erik Learned-Miller;Gary B. Huang;Aruni RoyChowdhury;Haoxiang Li

  • Names and faces in the news

    T.L. Berg;A.C. Berg;J. Edwards;M. Maire

  • Learning hierarchical representations for face verification with convolutional deep belief networks

    Gary B. Huang;Honglak Lee;Erik Learned-Miller

  • Unsupervised Joint Alignment of Complex Images

    G.B. Huang;V. Jain;E. Learned-Miller

  • Data driven image models through continuous joint alignment

    E.G. Learned-Miller

  • In Defense of Grid Features for Visual Question Answering

    Huaizu Jiang;Ishan Misra;Marcus Rohrbach;Erik Learned-Miller

  • ICA using spacings estimates of entropy

    Erik G. Learned-Miller;John W. Fisher

  • Learning to Align from Scratch

    Gary Huang;Marwan Mattar;Honglak Lee;Erik G. Learned-miller

  • Pixel-Adaptive Convolutional Neural Networks

    Hang Su;Varun Jampani;Deqing Sun;Orazio Gallo

  • Combining Local and Global Image Features for Object Class Recognition

    D.A. Lisin;M.A. Mattar;M.B. Blaschko;E.G. Learned-Miller

  • Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples

    Haw-Shiuan Chang;Erik G. Learned-Miller;Andrew McCallum

  • Scene Text Recognition Using Similarity and a Lexicon with Sparse Belief Propagation

    J.J. Weinman;E. Learned-Miller;A.R. Hanson

  • Augmenting CRFs with Boltzmann Machine Shape Priors for Image Labeling

    Andrew Kae;Kihyuk Sohn;Honglak Lee;Erik Learned-Miller

  • Online domain adaptation of a pre-trained cascade of classifiers

    Vidit Jain;Erik Learned-Miller

  • From Neural Re-Ranking to Neural Ranking: Learning a Sparse Representation for Inverted Indexing

    Hamed Zamani;Mostafa Dehghani;W. Bruce Croft;Erik Learned-Miller

  • Learning hierarchical representations for face verification

    Gary B. Huang;Honglak Lee;Erik G Learned-Miller

Frequent Co-Authors

Allen R. Hanson
Allen R. Hanson University of Massachusetts Amherst
Subhransu Maji
Subhransu Maji University of Massachusetts Amherst
Deqing Sun
Deqing Sun Google (United States)
Jan Kautz
Jan Kautz Nvidia (United States)
Evangelos Kalogerakis
Evangelos Kalogerakis Technical University of Crete
Andrew McCallum
Andrew McCallum University of Massachusetts Amherst
Honglak Lee
Honglak Lee University of Michigan–Ann Arbor
Roderic A. Grupen
Roderic A. Grupen University of Massachusetts Amherst

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