World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
56
Citations
16431
World Ranking
3997
National Ranking
1904

Overview

Ahmed Elgammal is affiliated with Rutgers, The State University of New Jersey in the United States. Their research primarily spans the field of Computer Science, with a focus on several subfields including Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design, Cognitive Neuroscience, Artificial Intelligence, and Cancer Research.

The scientist has contributed extensively to research on topics such as Generative Adversarial Networks and Image Synthesis, Computer Graphics and Visualization Techniques, Aesthetic Perception and Analysis, Advanced Vision and Imaging, Advanced Image Processing Techniques, Image Retrieval and Classification Techniques, and Digital Media Forensic Detection.

Ahmed Elgammal's frequent collaborators include Kunpeng Song, Bingchen Liu, Yizhe Zhu, Gerard de Melo, and Marian Mazzone.

Notable recent papers authored or co-authored by Ahmed Elgammal include:

  • Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis (2021), arXiv (Cornell University)
  • TIME: Text and Image Mutual-Translation Adversarial Networks (2021), Proceedings of the AAAI Conference on Artificial Intelligence
  • OOGAN: Disentangling GAN with One-Hot Sampling and Orthogonal Regularization (2020), Proceedings of the AAAI Conference on Artificial Intelligence
  • Self-Supervised Sketch-to-Image Synthesis (2021), Proceedings of the AAAI Conference on Artificial Intelligence
  • Spatial Frequency Bias in Convolutional Generative Adversarial Networks (2022), Proceedings of the AAAI Conference on Artificial Intelligence

Their publications appear mainly in venues such as arXiv (Cornell University), with 11 publications, and the Proceedings of the AAAI Conference on Artificial Intelligence, with 5 publications. Other venues include Artnodes, Electronic Imaging, and Clinical Lymphoma Myeloma & Leukemia.

Best Publications

  • Non-parametric Model for Background Subtraction

    Ahmed M. Elgammal;David Harwood;Larry S. Davis

  • Background and foreground modeling using nonparametric kernel density estimation for visual surveillance

    A. Elgammal;R. Duraiswami;D. Harwood;L.S. Davis

  • Inferring 3D body pose from silhouettes using activity manifold learning

    A. Elgammal;Chan-Su Lee

  • CAN: Creative adversarial networks generating "Art" by learning about styles and deviating from style norms

    Ahmed M. Elgammal;Bingchen Liu;Mohamed Elhoseiny;Marian Mazzone

  • A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts

    Yizhe Zhu;Mohamed Elhoseiny;Bingchen Liu;Xi Peng

  • Art, Creativity, and the Potential of Artificial Intelligence

    Marian Mazzone;Ahmed Elgammal

  • Write a Classifier: Zero-Shot Learning Using Purely Textual Descriptions

    Mohamed Elhoseiny;Babak Saleh;Ahmed Elgammal

  • SPDA-CNN: Unifying Semantic Part Detection and Abstraction for Fine-Grained Recognition

    Han Zhang;Tao Xu;Mohamed Elhoseiny;Xiaolei Huang

  • Efficient kernel density estimation using the fast gauss transform with applications to color modeling and tracking

    A. Elgammal;R. Duraiswami;L.S. Davis

  • Probabilistic framework for segmenting people under occlusion

    A.E. Elgammal;L.S. Davis

  • Separating style and content on a nonlinear manifold

    A. Elgammal;Chan-Su Lee

  • Probabilistic tracking in joint feature-spatial spaces

    A. Elgammal;R. Duraiswami;L.S. Davis

  • Graphical Contrastive Losses for Scene Graph Parsing

    Ji Zhang;Kevin J. Shih;Ahmed Elgammal;Andrew Tao

  • High Resolution Acquisition, Learning and Transfer of Dynamic 3‐D Facial Expressions

    Yang Wang;Xiaolei Huang;Chan-Su Lee;Song Zhang

  • Method for detecting a face in a digital image

    Mohammed Abdel-Mottaleb;Ahmed Elgammal

  • Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature

    Babak Saleh;Ahmed M. Elgammal

  • Modeling View and Posture Manifolds for Tracking

    Chan-Su Lee;A. Elgammal

  • Indoor Localization Using Camera Phones

    Nishkam Ravi;Pravin Shankar;Andrew Frankel;Ahmed Elgammal

  • Large-Scale Visual Relationship Understanding

    Ji Zhang;Yannis Kalantidis;Marcus Rohrbach;Manohar Paluri

  • Learning dynamics for exemplar-based gesture recognition

    A. Elgammal;V. Shet;Y. Yacoob;L.S. Davis

  • Skin Detection -a Short Tutorial

    John Daugman;Stephanie Schuckers;Karthik Nandakumar;Ryan N. Rakvic

Frequent Co-Authors

Larry S. Davis
Larry S. Davis University of Maryland, College Park
Dimitris N. Metaxas
Dimitris N. Metaxas Rutgers, The State University of New Jersey
Ramani Duraiswami
Ramani Duraiswami University of Maryland, College Park
Xi Peng
Xi Peng Sichuan University
Scott Cohen
Scott Cohen Adobe Systems (United States)
Mohamed Abdel-Mottaleb
Mohamed Abdel-Mottaleb University of Miami
Brian Price
Brian Price Adobe Systems (United States)
Bryan Catanzaro
Bryan Catanzaro Nvidia (United States)
Gerard de Melo
Gerard de Melo Hasso Plattner Institute
Ali Farhadi
Ali Farhadi University of Washington

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