World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
68
Citations
35083
World Ranking
2023
National Ranking
1023

Overview

Marcus Rohrbach is a researcher primarily affiliated with Facebook in the United States. Their work is concentrated in the field of Computer Science, with a strong focus on Computer Vision and Pattern Recognition, Artificial Intelligence, and related subfields.

Their recent scholarly contributions include publications in several notable venues:

  • FLAVA: A Foundational Language And Vision Alignment Model, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Learning To Recognize Procedural Activities with Distant Supervision, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • FLAVA: A Foundational Language And Vision Alignment Model, 2021, arXiv (Cornell University)
  • TextCaps: A Dataset for Image Captioning with Reading Comprehension, 2020, Lecture notes in computer science
  • In Defense of Grid Features for Visual Question Answering, 2020, arXiv (Cornell University)

Marcus Rohrbach's research output has a notable presence in publication venues such as:

  • arXiv (Cornell University)
  • Lecture notes in computer science
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Applied AI Letters

Their work spans several research topics including:

  • Multimodal Machine Learning Applications
  • Human Pose and Action Recognition
  • Domain Adaptation and Few-Shot Learning
  • Advanced Image and Video Retrieval Techniques
  • Video Analysis and Summarization
  • Anomaly Detection Techniques and Applications
  • Advanced Neural Network Applications

Frequent collaborators in their research include Laura Sevilla-Lara, Frank Keller, Shreyank N Gowda, Trevor Darrell, and Suzanne Petryk.

Marcus Rohrbach's body of work demonstrates sustained contributions to advancing methodologies and applications within Computer Vision and Artificial Intelligence. Their research on foundational alignment models for language and vision, procedural activity recognition, and datasets for image captioning reflects a broad engagement with current challenges in the field.

Best Publications

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

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

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

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

  • Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding

    Akira Fukui;Dong Huk Park;Daylen Yang;Anna Rohrbach

  • Sequence to Sequence -- Video to Text

    Subhashini Venugopalan;Marcus Rohrbach;Jeffrey Donahue;Raymond Mooney

  • Memory Aware Synapses: Learning What (not) to Forget

    Rahaf Aljundi;Francesca Babiloni;Mohamed Elhoseiny;Marcus Rohrbach

  • Neural Module Networks

    Jacob Andreas;Marcus Rohrbach;Trevor Darrell;Dan Klein

  • Translating Videos to Natural Language Using Deep Recurrent Neural Networks

    Subhashini Venugopalan;Huijuan Xu;Jeff Donahue;Marcus Rohrbach

  • FLAVA: A Foundational Language And Vision Alignment Model

    Unknown

  • Ask Your Neurons: A Neural-Based Approach to Answering Questions about Images

    Mateusz Malinowski;Marcus Rohrbach;Mario Fritz

  • Efficient Lifelong Learning with A-GEM

    Arslan Chaudhry;Marc'Aurelio Ranzato;Marcus Rohrbach;Mohamed Elhoseiny

  • A database for fine grained activity detection of cooking activities

    Marcus Rohrbach;Sikandar Amin;Mykhaylo Andriluka;Bernt Schiele

  • Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks With Octave Convolution

    Yunpeng Chen;Haoqi Fan;Bing Xu;Zhicheng Yan

  • Generating Visual Explanations

    Lisa Anne Hendricks;Zeynep Akata;Marcus Rohrbach;Marcus Rohrbach;Jeff Donahue

  • Grounding of Textual Phrases in Images by Reconstruction

    Anna Rohrbach;Marcus Rohrbach;Marcus Rohrbach;Ronghang Hu;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

  • Decoupling Representation and Classifier for Long-Tailed Recognition

    Bingyi Kang;Saining Xie;Marcus Rohrbach;Zhicheng Yan

  • Learning to Compose Neural Networks for Question Answering

    Jacob Andreas;Marcus Rohrbach;Trevor Darrell;Dan Klein

  • A dataset for Movie Description

    Anna Rohrbach;Marcus Rohrbach;Niket Tandon;Bernt Schiele

  • Graph-Based Global Reasoning Networks

    Yunpeng Chen;Marcus Rohrbach;Zhicheng Yan;Yan Shuicheng

  • On Tiny Episodic Memories in Continual Learning

    Arslan Chaudhry;Marcus Rohrbach;Mohamed Elhoseiny;Thalaiyasingam Ajanthan

Frequent Co-Authors

Trevor Darrell
Trevor Darrell University of California, Berkeley
Bernt Schiele
Bernt Schiele Max Planck Institute for Informatics
Anna Rohrbach
Anna Rohrbach Technical University of Darmstadt
Kate Saenko
Kate Saenko Boston University
Devi Parikh
Devi Parikh Facebook (United States)
Xinlei Chen
Xinlei Chen Facebook (United States)
Dhruv Batra
Dhruv Batra Georgia Institute of Technology
Jeff Donahue
Jeff Donahue DeepMind (United Kingdom)
Raymond J. Mooney
Raymond J. Mooney The University of Texas at Austin

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