Philipp Krähenbühl

Department of Computer Science
University of Texas at Austin
2317 Speedway
Austin, TX 78712-1757

email: philkr (at) cs.utexas.edu
CV, DBLP, Google Scholar, github

Research

I am an Assistant Professor in the Department of Computer Science at the University of Texas at Austin. I received my PhD in 2014 from the CS Department at Stanford University and then spent two wonderful years as a PostDoc at UC Berkeley.

My research interests lie in Computer Vision, Machine learning and Computer Graphics. I’m particularly interested in deep learning, image, video and scene understanding.

Publications

2023
PartDistillation: Learning Parts from Instance Segmentation
Jang Hyun Cho, Philipp Krähenbühl, Vignesh Ramanathan
CVPR 2023
[pdf] [details] [code]
Star
Learning Video Representations from Large Language Models
Yue Zhao, Ishan Misra, Philipp Krähenbühl, Rohit Girdhar
CVPR 2023
[pdf] [details] [code]
Star
2022
Real-Time Online Video Detection with Temporal Smoothing Transformers
Yue Zhao, Philipp Krähenbühl
ECCV 2022
[pdf] [details] [code]
Star
Long-tail detection with effective class-margins
Jang Hyun Cho, Philipp Krähenbühl
ECCV 2022
[pdf] [details] [code]
Star
Detecting twenty-thousand classes using image-level supervision
Xingyi Zhou, Rohit Girdhar, Armand Joulin, Philipp Krähenbühl, Ishan Misra
ECCV 2022
[pdf] [details] [code]
Star
Learning from All Vehicles
Dian Chen, Philipp Krähenbühl
CVPR 2022
[pdf] [details] [code]
Star
Cross-view Transformers for real-time Map-view Semantic Segmentation
Brady Zhou, Philipp Krähenbühl
CVPR 2022
[pdf] [details] [code]
Star
Global Tracking Transformers
Xingyi Zhou, Tianwei Yin, Vladlen Koltun, Philipp Krähenbühl
CVPR 2022
[pdf] [details] [code]
Star
Simple multi-dataset detection
Xingyi Zhou, Vladlen Koltun, Philipp Krähenbühl
CVPR 2022
[pdf] [details] [code]
Star
2021
Multimodal Virtual Point 3D Detection
Tianwei Yin, Xingyi Zhou, Philipp Krähenbühl
NeurIPS 2021
[pdf] [details] [code]
Learning to drive from a world on rails
Dian Chen, Vladlen Koltun, Philipp Krähenbühl
ICCV 2021
[pdf] [details]
Towards Long-Form Video Understanding
Chao-Yuan Wu, Philipp Krähenbühl
CVPR 2021
[pdf] [details]
Center-based 3d object detection and tracking
Tianwei Yin, Xingyi Zhou, Philipp Krähenbühl
CVPR 2021
[pdf] [details] [code]
Star
Memory Optimization for Deep Networks
Aashaka Shah, Chao-Yuan Wu, Jayashree Mohan, Vijay Chidambaram, Philipp Krähenbühl
ICLR 2021
[pdf] [details] [code]
Star
2020
Domain Adaptation Through Task Distillation
Brady Zhou, Nimit Kalra, Philipp Krähenbühl
ECCV 2020
[pdf] [details] [code]
Star
Tracking Objects as Points
Xingyi Zhou, Vladlen Koltun, Philipp Krähenbühl
ECCV 2020
[pdf] [details] [code]
Star
A Multigrid Method for Efficiently Training Video Models
Chao-Yuan Wu, Ross Girshick, Kaiming He, Christoph Feichtenhofer, Philipp Krähenbühl
CVPR 2020
[pdf] [details] [code]
Star
2019
Learning by Cheating
Dian Chen, Brady Zhou, Vladlen Koltun, Philipp Krähenbühl
CORL 2019
[pdf] [details] [code]
Star
Objects as points
Xingyi Zhou, Dequan Wang, Philipp Krähenbühl
arXiv preprint arXiv:1904.07850 2019
[pdf] [details] [code]
Star
Monocular plan view networks for autonomous driving
Dequan Wang, Coline Devin, Qi-Zhi Cai, Philipp Krähenbühl, Trevor Darrell
IROS 2019
[pdf] [details]
Long-Term Feature Banks for Detailed Video Understanding
Chao-Yuan Wu, Christoph Feichtenhofer, Haoqi Fan, Kaiming He, Philipp Krähenbühl, Ross Girshick
CVPR 2019
[pdf] [details] [supplement] [code]
Star
Bottom-up Object Detection by Grouping Extreme and Center Points
Xingyi Zhou, Jiacheng Zhuo, Philipp Krähenbühl
CVPR 2019
[pdf] [details] [supplement] [code]
Star
Joint Monocular 3D Vehicle Detection and Tracking
Hou-Ning Hu, Qi-Zhi Cai, Dequan Wang, Ji Lin, Min Sun, Philipp Krähenbühl, Trevor Darrell, Fisher Yu
ICCV 2019
[pdf] [details] [code]
Star
Does Computer Vision Matter for Action?
Brady Zhou, Philipp Krähenbühl, Vladlen Koltun
Science Robotics 2019
[pdf] [details] [code]
Star
Don't let your Discriminator be fooled
Brady Zhou, Philipp Krähenbühl
ICLR 2019
[pdf] [details]
2018
Video Compression through Image Interpolation
Chao-Yuan Wu, Nayan Singhal, Philipp Krähenbühl
ECCV 2018
[pdf] [details] [code]
Star
Domain transfer through deep activation matching
Haoshuo Huang,Qixing Huang, Philipp Krähenbühl
ECCV 2018
[pdf] [details] [project]
Compressed Video Action Recognition
Chao-Yuan Wu,Manzil Zaheer,Hexiang Hu,R. Manmatha,Alexander J. Smola, Philipp Krähenbühl
CVPR 2018
[pdf] [details] [project] [code]
Star
Free Supervision from Video Games
Philipp Krähenbühl
CVPR 2018
[pdf] [details] [project] [code]
Star
2017
Sampling Matters in Deep Embedding Learning
Chao-Yuan Wu, R. Manmatha, Alexander J. Smola, Philipp Krähenbühl
ICCV 2017
[pdf] [details] [project] [code]
Star
Adversarial Feature Learning
Jeff Donahue, Philipp Krähenbühl, Trevor Darrell
ICLR 2017
[pdf] [details] [code]
Star
2016
Generative Visual Manipulationon the Natural Image Manifold
Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, Alexei A. Efros
ECCV 2016
[pdf] [details] [project] [code]
Star
Context Encoders: Feature Learning by Inpainting
Deepak Pathak, Philipp Krähenbühl, Jeff Donahue, Trevor Darrell, Alyosha Efros
CVPR 2016
[pdf] [details] [project] [code]
Star
Learning Dense Correspondence via 3D-guided Cycle Consistency
Tinghui Zhou, Philipp Krähenbühl, Mathieu Aubry, Qixing Huang, Alyosha Efros
CVPR 2016
[pdf] [details] [project]
Data-dependent initializations of convolutional neural networks
Philipp Krähenbühl, Carl Doersch, Jeff Donahue, Trevor Darrell
ICLR 2016
[pdf] [details] [py-faster-rcnn training scripts] [code]
Star
2015
Learning a Discriminative Model for the Perception of Realism in Composite Images
Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, Alyosha Efros
ICCV 2015
[pdf] [details] [code]
Star
Learning Data-driven Reflectance Priors for Intrinsic Image Decomposition
Tinghui Zhou, Philipp Krähenbühl, Alyosha Efros
ICCV 2015
[pdf] [details] [code]
Star
Constrained Convolutional Neural Networks for Weakly Supervised Segmentation
Deepak Pathak, Philipp Krähenbühl, Trevor Darrell
ICCV 2015
[pdf] [details] [supplement] [code]
Star
Learning to propose objects
Philipp Krähenbühl, Vladlen Koltun
CVPR 2015
[pdf] [details] [code]
Star
2014
Geodesic Object Proposals
Philipp Krähenbühl, Vladlen Koltun
ECCV 2014
[pdf] [details] [data] [code]
2013
Parameter Learning and Convergent Inference for Dense Random Fields
Philipp Krähenbühl, Vladlen Koltun
ICML 2013
[pdf] [details] [project] [code]
2012
Efficient Nonlocal regularization for Optical Flow
Philipp Krähenbühl, Vladlen Koltun
ECCV 2012
[pdf] [details]
Saliency Filters: Contrast Based Filtering for Salient Region Detection
Federico Perazzi, Philipp Krähenbühl, Yael Pritch, Alexander Hornung
CVPR 2012
[pdf] [details] [project] [code]
2011
Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
Philipp Krähenbühl, Vladlen Koltun
NIPS 2011
[pdf] [details] [project] [code]
2010
Gesture Controllers
Sergey Levine, Philipp Krähenbühl, Sebastian Thrun, Vladlen Koltun
SIGGRAPH 2010
[pdf] [details]
2009
A system for retargeting of streaming video
Philipp Krähenbühl, Manuel Lang, Alexander Hornung, Markus Gross
SIGGRAPH Asia 2009
[pdf] [details]

Research group

PhD Students:

Past PhD students:

Undergraduates and MS:

Past undergraduates and MS:

Teaching

Joining my research group

UT CS or ECE students: I’d recomment you to take my graduate deep learning class (CS395T), and start working with me through that class.

Prospective students: Please read about our graduate admissions process and state your interested in my research group in your statement of purpose. Please do not contact me directly. The statistics are not in your favor either. We have not yet admitted a single student to UTCS who contacted me directly.

About my last name

I’m well aware that my last name is not the easiest one to write or cite (and I saw it butchered a bunch of times over the years). So to make things easier just pick your document type below and copy the string:

Regular text

Krähenbühl

Latex & Bibtex

Kr\"ahenb\"uhl

HTML

Krähenbühl

If all the above fail, just use Kraehenbuehl.