World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
100
Citations
37646
World Ranking
374
National Ranking
206

Research.com Recognitions

  • 2019 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to computer vision in visual recognition and search.
  • 2018 - IAPR J. K. Aggarwal Prize "For contributions to image matching and retrieval."
  • 2012 - Fellow of Alfred P. Sloan Foundation

Overview

Kristen Grauman is affiliated with The University of Texas at Austin in the United States. Their research primarily focuses on the field of Computer Science, with a specialization in Computer Vision and Pattern Recognition. The work spans several subfields including Signal Processing, Artificial Intelligence, Cognitive Neuroscience, and Control and Systems Engineering.

The scientist has published extensively, with a total of 216 publications within Computer Science. Among these, notable contributions have been made in topics such as Human Pose and Action Recognition, Multimodal Machine Learning Applications, Music and Audio Processing, Speech and Audio Processing, Video Analysis and Summarization, Video Surveillance and Tracking Methods, and Advanced Vision and Imaging.

Recent research papers authored or co-authored by Kristen Grauman include:

  • "Ego4D: Around the World in 3,000 Hours of Egocentric Video," 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "Audiovisual SlowFast Networks for Video Recognition," 2020, arXiv (Cornell University)
  • "PONI: Potential Functions for ObjectGoal Navigation with Interaction-free Learning," 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "Learning patterns of tourist movement and photography from geotagged photos at archaeological heritage sites in Cuzco, Peru," 2020, Tourism Management
  • "Zero Experience Required: Plug & Play Modular Transfer Learning for Semantic Visual Navigation," 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Frequent co-authors in their research collaborations include Ziad Al-Halah, Santhosh Kumar Ramakrishnan, Changan Chen, Tushar Nagarajan, and Zihui Xue.

Kristen Grauman's publications have appeared predominantly in the following venues:

  • arXiv (Cornell University)
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • International Journal of Computer Vision

The scientist has received recognition through several awards including being named a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) in 2019 for contributions in visual recognition and search, the IAPR J. K. Aggarwal Prize in 2018 for work related to image matching and retrieval, and Fellowship of the Alfred P. Sloan Foundation in 2012.

Best Publications

  • Geodesic flow kernel for unsupervised domain adaptation

    Boqing Gong;Yuan Shi;Fei Sha;Kristen Grauman

  • The pyramid match kernel: discriminative classification with sets of image features

    K. Grauman;T. Darrell

  • Relative attributes

    Devi Parikh;Kristen Grauman

  • Kernelized locality-sensitive hashing for scalable image search

    Brian Kulis;Kristen Grauman

  • Observe locally, infer globally: A space-time MRF for detecting abnormal activities with incremental updates

    Jaechul Kim;Kristen Grauman

  • Discovering important people and objects for egocentric video summarization

    Yong Jae Lee;Joydeep Ghosh;Kristen Grauman

  • Video Summarization with Long Short-Term Memory

    Ke Zhang;Wei-Lun Chao;Fei Sha;Kristen Grauman

  • Learning a hierarchy of discriminative space-time neighborhood features for human action recognition

    Adriana Kovashka;Kristen Grauman

  • Story-Driven Summarization for Egocentric Video

    Zheng Lu;Kristen Grauman

  • Key-segments for video object segmentation

    Yong Jae Lee;Jaechul Kim;Kristen Grauman

  • Fine-Grained Visual Comparisons with Local Learning

    Aron Yu;Kristen Grauman

  • Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation

    Boqing Gong;Kristen Grauman;Fei Sha

  • The Pyramid Match Kernel: Efficient Learning with Sets of Features

    Kristen Grauman;Trevor Darrell

  • BlockDrop: Dynamic Inference Paths in Residual Networks

    Zuxuan Wu;Tushar Nagarajan;Abhishek Kumar;Steven Rennie

  • SpotTune: Transfer Learning Through Adaptive Fine-Tuning

    Yunhui Guo;Honghui Shi;Abhishek Kumar;Kristen Grauman

  • Ego4D: Around the World in 3,000 Hours of Egocentric Video

    Kristen Grauman;Andrew Westbury;Eugene Byrne;Zachary Chavis

  • VizWiz Grand Challenge: Answering Visual Questions from Blind People

    Danna Gurari;Qing Li;Abigale J. Stangl;Anhong Guo

  • Diverse Sequential Subset Selection for Supervised Video Summarization

    Boqing Gong;Wei-Lun Chao;Kristen Grauman;Fei Sha

  • Active Learning with Gaussian Processes for Object Categorization

    Ashish Kapoor;Kristen Grauman;Raquel Urtasun;Trevor Darrell

  • FusionSeg: Learning to Combine Motion and Appearance for Fully Automatic Segmentation of Generic Objects in Videos

    Suyog Dutt Jain;Bo Xiong;Kristen Grauman

  • Kernelized Locality-Sensitive Hashing

    B. Kulis;K. Grauman

  • Learning with Whom to Share in Multi-task Feature Learning

    Zhuoliang Kang;Kristen Grauman;Fei Sha

Frequent Co-Authors

Fei Sha
Fei Sha Facebook (United States)
Trevor Darrell
Trevor Darrell University of California, Berkeley
Yong Jae Lee
Yong Jae Lee University of Wisconsin–Madison
Devi Parikh
Devi Parikh Facebook (United States)
Boqing Gong
Boqing Gong Google (United States)
Margrit Betke
Margrit Betke Boston University
Rogerio Feris
Rogerio Feris IBM (United States)
Prateek Jain
Prateek Jain Google (United States)
Christoph Feichtenhofer
Christoph Feichtenhofer Meta Platforms, Inc.
Brian Kulis
Brian Kulis Boston University

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