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D-Index & Metrics

Computer Science

D-Index
83
Citations
56673
World Ranking
878
National Ranking
479

Overview

Kate Saenko is affiliated with Boston University in the United States. Their research contributions are predominantly in the field of Computer Science, with a focus on Computer Vision and Pattern Recognition as well as Artificial Intelligence. Their work also spans into subfields related to Cancer Research, Radiology, Nuclear Medicine and Imaging, and Media Technology.

Their research topics cover several key areas including:

  • Multimodal Machine Learning Applications
  • Domain Adaptation and Few-Shot Learning
  • Human Pose and Action Recognition
  • Advanced Neural Network Applications
  • Advanced Image and Video Retrieval Techniques
  • Topic Modeling
  • Generative Adversarial Networks and Image Synthesis

Kate Saenko has published extensively, contributing to diverse high-impact venues. Frequent publication venues include:

  • arXiv (Cornell University)
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Zenodo (CERN European Organization for Nuclear Research)
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Springer proceedings in advanced robotics

Some of their recent papers include:

  • "Universal Domain Adaptation through Self Supervision" (2020), published in arXiv (Cornell University)
  • "Real-Time Semantic Segmentation With Fast Attention" (2020), published in IEEE Robotics and Automation Letters
  • "Adapting Deep Visuomotor Representations with Weak Pairwise Constraints" (2020), published in Springer proceedings in advanced robotics
  • "Learning Cross-Modal Contrastive Features for Video Domain Adaptation" (2021), published in 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • "ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes" (2022), published in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Throughout their career, they have collaborated frequently with a number of co-authors, notably:

  • Bryan A. Plummer
  • Stan Sclaroff
  • Rogério Feris
  • Kuniaki Saito
  • Donghyun Kim

Best Publications

  • Long-term recurrent convolutional networks for visual recognition and description

    Jeff Donahue;Lisa Anne Hendricks;Sergio Guadarrama;Marcus Rohrbach

  • Adversarial Discriminative Domain Adaptation

    Eric Tzeng;Judy Hoffman;Kate Saenko;Trevor Darrell

  • Deep CORAL: Correlation Alignment for Deep Domain Adaptation

    Baochen Sun;Kate Saenko

  • Adapting visual category models to new domains

    Kate Saenko;Brian Kulis;Mario Fritz;Trevor Darrell

  • 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

  • Return of frustratingly easy domain adaptation

    Baochen Sun;Jiashi Feng;Kate Saenko

  • Long-Term Recurrent Convolutional Networks for Visual Recognition and Description

    Jeff Donahue;Lisa Anne Hendricks;Marcus Rohrbach;Subhashini Venugopalan

  • Sequence to Sequence -- Video to Text

    Subhashini Venugopalan;Marcus Rohrbach;Jeffrey Donahue;Raymond Mooney

  • Moment Matching for Multi-Source Domain Adaptation

    Xingchao Peng;Qinxun Bai;Xide Xia;Zijun Huang

  • Simultaneous Deep Transfer Across Domains and Tasks

    Eric Tzeng;Judy Hoffman;Trevor Darrell;Kate Saenko

  • Translating Videos to Natural Language Using Deep Recurrent Neural Networks

    Subhashini Venugopalan;Huijuan Xu;Jeff Donahue;Marcus Rohrbach

  • Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering

    Huijuan Xu;Kate Saenko

  • What you saw is not what you get: Domain adaptation using asymmetric kernel transforms

    Brian Kulis;Kate Saenko;Trevor Darrell

  • R-C3D: Region Convolutional 3D Network for Temporal Activity Detection

    Huijuan Xu;Abir Das;Kate Saenko

  • Strong-Weak Distribution Alignment for Adaptive Object Detection

    Kuniaki Saito;Yoshitaka Ushiku;Tatsuya Harada;Kate Saenko

  • VisDA: The Visual Domain Adaptation Challenge

    Xingchao Peng;Ben Usman;Neela Kaushik;Judy Hoffman

  • Semi-Supervised Domain Adaptation via Minimax Entropy

    Kuniaki Saito;Donghyun Kim;Stan Sclaroff;Trevor Darrell

  • Learning to Reason: End-to-End Module Networks for Visual Question Answering

    Ronghang Hu;Jacob Andreas;Marcus Rohrbach;Trevor Darrell

  • Natural Language Object Retrieval

    Ronghang Hu;Huazhe Xu;Marcus Rohrbach;Jiashi Feng

  • YouTube2Text: Recognizing and Describing Arbitrary Activities Using Semantic Hierarchies and Zero-Shot Recognition

    Sergio Guadarrama;Niveda Krishnamoorthy;Girish Malkarnenkar;Subhashini Venugopalan

Frequent Co-Authors

Trevor Darrell
Trevor Darrell University of California, Berkeley
Judy Hoffman
Judy Hoffman Georgia Institute of Technology
Stan Sclaroff
Stan Sclaroff Boston University
Marcus Rohrbach
Marcus Rohrbach Facebook (United States)
Jeff Donahue
Jeff Donahue DeepMind (United Kingdom)
Raymond J. Mooney
Raymond J. Mooney The University of Texas at Austin
Anna Rohrbach
Anna Rohrbach Technical University of Darmstadt
Mario Fritz
Mario Fritz Helmholtz Center for Information Security
Brian Kulis
Brian Kulis Boston University

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