World's Best Scientists 2026 revealed!

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Computer Science

D-Index
43
Citations
26827
World Ranking
7734
National Ranking
3338

Overview

Philipp Krähenbühl is affiliated with The University of Texas at Austin in the United States. Their research work primarily belongs to the field of Computer Science, with a strong focus on Computer Vision and Pattern Recognition.

Their research spans various subfields, including Artificial Intelligence, Molecular Biology, Aerospace Engineering, and Automotive Engineering. This variety reflects interdisciplinary interests alongside a core concentration on visual computing and machine learning techniques.

Krähenbühl has contributed to topics such as:

  • Advanced Neural Network Applications
  • Advanced Image and Video Retrieval Techniques
  • Domain Adaptation and Few-Shot Learning
  • Multimodal Machine Learning Applications
  • Human Pose and Action Recognition
  • Video Surveillance and Tracking Methods
  • Natural Language Processing Techniques

The scientist's recent notable publications include:

  • Cross-view Transformers for real-time Map-view Semantic Segmentation, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Global Tracking Transformers, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Probabilistic two-stage detection, 2021, arXiv (Cornell University)
  • Multimodal Virtual Point 3D Detection, 2021, arXiv (Cornell University)
  • Simple Multi-dataset Detection, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Krähenbühl frequently publishes in venues such as:

  • arXiv (Cornell University)
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Frequently collaborating researchers with Krähenbühl include:

  • Xingyi Zhou
  • Vladlen Koltun
  • Jang Hyun Cho
  • Brady Zhou
  • Tianwei Yin

Their work involves a mix of advanced methodologies for both theoretical and applied problems within computer vision, machine learning, and related areas.

Best Publications

  • Context Encoders: Feature Learning by Inpainting

    Deepak Pathak;Philipp Krahenbuhl;Jeff Donahue;Trevor Darrell

  • Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials

    Philipp Krähenbühl;Vladlen Koltun

  • Saliency filters: Contrast based filtering for salient region detection

    Federico Perazzi;Philipp Krahenbuhl;Yael Pritch;Alexander Hornung

  • Center-based 3D Object Detection and Tracking

    Tianwei Yin;Xingyi Zhou;Philipp Krahenbuhl

  • Generative Visual Manipulation on the Natural Image Manifold

    Jun-Yan Zhu;Philipp Krähenbühl;Eli Shechtman;Alexei A. Efros

  • Objects as Points

    Xingyi Zhou;Dequan Wang;Philipp Krähenbühl

  • Tracking Objects as Points

    Xingyi Zhou;Vladlen Koltun;Philipp Krähenbühl

  • Adversarial Feature Learning

    Jeff Donahue;Philipp Krähenbühl;Trevor Darrell

  • Bottom-Up Object Detection by Grouping Extreme and Center Points

    Xingyi Zhou;Jiacheng Zhuo;Philipp Krahenbuhl

  • Sampling Matters in Deep Embedding Learning

    R. Manmatha;Chao-Yuan Wu;Alexander J. Smola;Philipp Krahenbuhl

  • Constrained Convolutional Neural Networks for Weakly Supervised Segmentation

    Deepak Pathak;Philipp Krahenbuhl;Trevor Darrell

  • Long-Term Feature Banks for Detailed Video Understanding

    Chao-Yuan Wu;Christoph Feichtenhofer;Haoqi Fan;Kaiming He

  • Geodesic Object Proposals

    Philipp Krähenbühl;Vladlen Koltun

  • Learning Dense Correspondence via 3D-Guided Cycle Consistency

    Tinghui Zhou;Philipp Krahenbuhl;Mathieu Aubry;Qixing Huang

  • Compressed Video Action Recognition

    Chao-Yuan Wu;Manzil Zaheer;Hexiang Hu;R. Manmatha

  • Video Compression Through Image Interpolation

    Chao-Yuan Wu;Nayan Singhal;Philipp Krähenbühl

  • A system for retargeting of streaming video

    Philipp Krähenbühl;Manuel Lang;Alexander Hornung;Markus Gross

  • Global Tracking Transformers

    Unknown

  • Data-dependent Initializations of Convolutional Neural Networks

    Philipp Krähenbühl;Carl Doersch;Carl Doersch;Jeff Donahue;Trevor Darrell

  • Gesture controllers

    Sergey Levine;Philipp Krähenbühl;Sebastian Thrun;Vladlen Koltun

  • Assessing Generalization in Deep Reinforcement Learning

    Charles Packer;Katelyn Gao;Jernej Kos;Philipp Krähenbühl

  • Joint Monocular 3D Vehicle Detection and Tracking.

    Hou-Ning Hu;Qi-Zhi Cai;Dequan Wang;Ji Lin

Frequent Co-Authors

Vladlen Koltun
Vladlen Koltun Apple (United States)
Trevor Darrell
Trevor Darrell University of California, Berkeley
Alexei A. Efros
Alexei A. Efros University of California, Berkeley
Jeff Donahue
Jeff Donahue DeepMind (United Kingdom)
Kaiming He
Kaiming He Facebook (United States)
R. Manmatha
R. Manmatha Amazon (United States)
Alexander J. Smola
Alexander J. Smola Amazon (United States)
Eli Shechtman
Eli Shechtman Adobe Systems (United States)
Christoph Feichtenhofer
Christoph Feichtenhofer Meta Platforms, Inc.
Jun-Yan Zhu
Jun-Yan Zhu Carnegie Mellon University

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