World's Best Scientists 2026 revealed!

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Computer Science

D-Index
51
Citations
25222
World Ranking
5199
National Ranking
2387

Overview

Judy Hoffman is affiliated with the Georgia Institute of Technology in the United States. Their research primarily lies within the broad field of Computer Science, with a focus on several specialized subfields including Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Control and Systems Engineering, and Aerospace Engineering.

The scientist's work covers a range of main topics:

  • Domain Adaptation and Few-Shot Learning
  • Multimodal Machine Learning Applications
  • Advanced Neural Network Applications
  • Human Pose and Action Recognition
  • Adversarial Robustness in Machine Learning
  • Anomaly Detection Techniques and Applications
  • Video Surveillance and Tracking Methods

Judy Hoffman has coauthored works frequently with several researchers, including:

  • Viraj Prabhu
  • Prithvijit Chattopadhyay
  • Daniel Bolya
  • Duen Horng Chau
  • Kate Saenko

The venues where Judy Hoffman publishes often include:

  • arXiv (Cornell University)
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Lecture notes in computer science
  • Annals of Mathematics and Artificial Intelligence
  • Springer proceedings in advanced robotics

Recent papers authored or coauthored by Judy Hoffman include:

  • Adapting Deep Visuomotor Representations with Weak Pairwise Constraints, 2020, Springer proceedings in advanced robotics
  • Token Merging: Your ViT But Faster, 2022, arXiv (Cornell University)
  • Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles, 2023, arXiv (Cornell University)
  • ZSON: Zero-Shot Object-Goal Navigation using Multimodal Goal Embeddings, 2022, arXiv (Cornell University)
  • Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks, 2023, arXiv (Cornell University)

Best Publications

  • Adversarial Discriminative Domain Adaptation

    Eric Tzeng;Judy Hoffman;Kate Saenko;Trevor Darrell

  • DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

    Jeff Donahue;Yangqing Jia;Oriol Vinyals;Judy Hoffman

  • Deep Domain Confusion: Maximizing for Domain Invariance

    Eric Tzeng;Judy Hoffman;Ning Zhang;Kate Saenko

  • CyCADA: Cycle-Consistent Adversarial Domain Adaptation

    Judy Hoffman;Eric Tzeng;Taesung Park;Jun-Yan Zhu

  • Simultaneous Deep Transfer Across Domains and Tasks

    Eric Tzeng;Judy Hoffman;Trevor Darrell;Kate Saenko

  • FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation

    Judy Hoffman;Dequan Wang;Fisher Yu;Trevor Darrell

  • VisDA: The Visual Domain Adaptation Challenge

    Xingchao Peng;Ben Usman;Neela Kaushik;Judy Hoffman

  • Cross Modal Distillation for Supervision Transfer

    Saurabh Gupta;Judy Hoffman;Jitendra Malik

  • Inferring and Executing Programs for Visual Reasoning

    Justin Johnson;Bharath Hariharan;Laurens van der Maaten;Judy Hoffman

  • LSDA: Large Scale Detection through Adaptation

    Judy Hoffman;Sergio Guadarrama;Eric S Tzeng;Ronghang Hu

  • Efficient Learning of Domain-invariant Image Representations

    Judy Hoffman;Erik Rodner;Jeff Donahue;Trevor Darrell

  • Learning with Side Information through Modality Hallucination

    Judy Hoffman;Saurabh Gupta;Trevor Darrell

  • Discovering Latent Domains for Multisource Domain Adaptation

    Judy Hoffman;Brian Kulis;Trevor Darrell;Kate Saenko

  • Clockwork Convnets for Video Semantic Segmentation

    Evan Shelhamer;Kate Rakelly;Judy Hoffman;Trevor Darrell

  • Label efficient learning of transferable representations across domains and tasks

    Zelun Luo;Yuliang Zou;Judy Hoffman;Li Fei-Fei

  • Semi-supervised Domain Adaptation with Instance Constraints

    Jeff Donahue;Judy Hoffman;Erik Rodner;Kate Saenko

  • Fine-Grained Recognition in the Wild: A Multi-task Domain Adaptation Approach

    Timnit Gebru;Judy Hoffman;Li Fei-Fei

  • VisDA: A Synthetic-to-Real Benchmark for Visual Domain Adaptation

    Xingchao Peng;Ben Usman;Neela Kaushik;Dequan Wang

  • TIDE: A General Toolbox for Identifying Object Detection Errors

    Daniel Bolya;Sean Foley;James Hays;Judy Hoffman

  • Continuous Manifold Based Adaptation for Evolving Visual Domains

    Judy Hoffman;Trevor Darrell;Kate Saenko

  • Predictive Inequity in Object Detection.

    Benjamin Wilson;Judy Hoffman;Jamie Morgenstern

Frequent Co-Authors

Trevor Darrell
Trevor Darrell University of California, Berkeley
Kate Saenko
Kate Saenko Boston University
Jeff Donahue
Jeff Donahue DeepMind (United Kingdom)
Li Fei-Fei
Li Fei-Fei Stanford University
Dhruv Batra
Dhruv Batra Georgia Institute of Technology
Saurabh Gupta
Saurabh Gupta University of Illinois at Urbana-Champaign
Ross Girshick
Ross Girshick Facebook (United States)
Irfan Essa
Irfan Essa Georgia Institute of Technology
Devi Parikh
Devi Parikh Facebook (United States)
Mehryar Mohri
Mehryar Mohri Google (United States)

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