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
2024 | |
---|---|
Distilling Vision-Language Models on Millions of Videos Yue Zhao, Long Zhao, Xingyi Zhou, Jialin Wu, Chun-Te Chu, Hui Miao, Florian Schroff, Hartwig Adam, Ting Liu, Boqing Gong, Philipp Krähenbühl, Liangzhe Yuan CVPR 2024 arxiv | |
Promptable Closed-loop Traffic Simulation Shuhan Tan, Boris Ivanovic, Yuxiao Chen, Boyi Li, Xinshuo Weng, Yulong Cao, Philipp Krähenbühl, Marco Pavone CoRL 2024 arxiv | |
2023 | |
PartDistillation: Learning Parts from Instance Segmentation Jang Hyun Cho, Philipp Krähenbühl, Vignesh Ramanathan CVPR 2023 code pdf | |
Learning Video Representations from Large Language Models Yue Zhao, Ishan Misra, Philipp Krähenbühl, Rohit Girdhar CVPR 2023 code arxiv | |
Language Conditioned Traffic Generation Shuhan Tan, Boris Ivanovic, Xinshuo Weng, Marco Pavone, Philipp Kraehenbuehl CoRL 2023 code arxiv | |
Language-conditioned Detection Transformer Jang Hyun Cho, Philipp Krähenbühl CVPR 2023 code arxiv | |
Predicting a Protein's Stability under a Million Mutations Jeffrey Ouyang-Zhang, Daniel J. Diaz, Adam R. Klivans, Philipp Krähenbühl NeurIPS 2023 code arxiv | |
2022 | |
Real-Time Online Video Detection with Temporal Smoothing Transformers Yue Zhao, Philipp Krähenbühl ECCV 2022 code arxiv | |
Long-tail detection with effective class-margins Jang Hyun Cho, Philipp Krähenbühl ECCV 2022 code arxiv | |
Detecting twenty-thousand classes using image-level supervision Xingyi Zhou, Rohit Girdhar, Armand Joulin, Philipp Krähenbühl, Ishan Misra ECCV 2022 code arxiv | |
Learning from All Vehicles Dian Chen, Philipp Krähenbühl CVPR 2022 code arxiv | |
Cross-view Transformers for real-time Map-view Semantic Segmentation Brady Zhou, Philipp Krähenbühl CVPR 2022 code arxiv | |
Global Tracking Transformers Xingyi Zhou, Tianwei Yin, Vladlen Koltun, Philipp Krähenbühl CVPR 2022 code arxiv | |
Simple multi-dataset detection Xingyi Zhou, Vladlen Koltun, Philipp Krähenbühl CVPR 2022 code arxiv | |
2021 | |
Multimodal Virtual Point 3D Detection Tianwei Yin, Xingyi Zhou, Philipp Krähenbühl NeurIPS 2021 code arxiv | |
Learning to drive from a world on rails Dian Chen, Vladlen Koltun, Philipp Krähenbühl ICCV 2021 arxiv | |
Towards Long-Form Video Understanding Chao-Yuan Wu, Philipp Krähenbühl CVPR 2021 arxiv | |
Center-based 3d object detection and tracking Tianwei Yin, Xingyi Zhou, Philipp Krähenbühl CVPR 2021 code arxiv | |
Memory Optimization for Deep Networks Aashaka Shah, Chao-Yuan Wu, Jayashree Mohan, Vijay Chidambaram, Philipp Krähenbühl ICLR 2021 code arxiv | |
Probabilistic two-stage detection Xingyi Zhou, Vladlen Koltun, Philipp Krähenbühl arXiv 2021 arxiv | |
2020 | |
Domain Adaptation Through Task Distillation Brady Zhou, Nimit Kalra, Philipp Krähenbühl ECCV 2020 code arxiv | |
Tracking Objects as Points Xingyi Zhou, Vladlen Koltun, Philipp Krähenbühl ECCV 2020 code arxiv | |
A Multigrid Method for Efficiently Training Video Models Chao-Yuan Wu, Ross Girshick, Kaiming He, Christoph Feichtenhofer, Philipp Krähenbühl CVPR 2020 code arxiv | |
2019 | |
Learning by Cheating Dian Chen, Brady Zhou, Vladlen Koltun, Philipp Krähenbühl CORL 2019 code arxiv | |
Monocular plan view networks for autonomous driving Dequan Wang, Coline Devin, Qi-Zhi Cai, Philipp Krähenbühl, Trevor Darrell IROS 2019 arxiv | |
Objects as points Xingyi Zhou, Dequan Wang, Philipp Krähenbühl arXiv preprint arXiv:1904.07850 2019 code arxiv | |
Long-Term Feature Banks for Detailed Video Understanding Chao-Yuan Wu, Christoph Feichtenhofer, Haoqi Fan, Kaiming He, Philipp Krähenbühl, Ross Girshick CVPR 2019 code arxiv supplement | |
Bottom-up Object Detection by Grouping Extreme and Center Points Xingyi Zhou, Jiacheng Zhuo, Philipp Krähenbühl CVPR 2019 code arxiv supplement | |
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 code arxiv | |
Does Computer Vision Matter for Action? Brady Zhou, Philipp Krähenbühl, Vladlen Koltun Science Robotics 2019 code arxiv | |
Don't let your Discriminator be fooled Brady Zhou, Philipp Krähenbühl ICLR 2019 | |
2018 | |
Video Compression through Image Interpolation Chao-Yuan Wu, Nayan Singhal, Philipp Krähenbühl ECCV 2018 code arxiv | |
Domain transfer through deep activation matching Haoshuo Huang, Qixing Huang, Philipp Krähenbühl ECCV 2018 pdf project | |
Compressed Video Action Recognition Chao-Yuan Wu,Manzil Zaheer,Hexiang Hu,R. Manmatha,Alexander J. Smola, Philipp Krähenbühl CVPR 2018 code arxiv project | |
Free Supervision from Video Games Philipp Krähenbühl CVPR 2018 code pdf project | |
Assessing Generalization in Deep Reinforcement Learning Charles Packer, Katelyn Gao, Jernej Kos, Philipp Krähenbühl, Vladlen Koltun, Dawn Song arXiv 2018 arxiv | |
2017 | |
Sampling Matters in Deep Embedding Learning Chao-Yuan Wu, R. Manmatha, Alexander J. Smola, Philipp Krähenbühl ICCV 2017 code arxiv project | |
Adversarial Feature Learning Jeff Donahue, Philipp Krähenbühl, Trevor Darrell ICLR 2017 code arxiv | |
2016 | |
Generative Visual Manipulation on the Natural Image Manifold Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, Alexei A. Efros ECCV 2016 code arxiv project | |
Context Encoders: Feature Learning by Inpainting Deepak Pathak, Philipp Krähenbühl, Jeff Donahue, Trevor Darrell, Alyosha Efros CVPR 2016 code arxiv project | |
Learning Dense Correspondence via 3D-guided Cycle Consistency Tinghui Zhou, Philipp Krähenbühl, Mathieu Aubry, Qixing Huang, Alyosha Efros CVPR 2016 arxiv project | |
Data-dependent initializations of convolutional neural networks Philipp Krähenbühl, Carl Doersch, Jeff Donahue, Trevor Darrell ICLR 2016 code arxiv | |
2015 | |
Learning a Discriminative Model for the Perception of Realism in Composite Images Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, Alyosha Efros ICCV 2015 code arxiv | |
Learning Data-driven Reflectance Priors for Intrinsic Image Decomposition Tinghui Zhou, Philipp Krähenbühl, Alyosha Efros ICCV 2015 code arxiv | |
Constrained Convolutional Neural Networks for Weakly Supervised Segmentation Deepak Pathak, Philipp Krähenbühl, Trevor Darrell ICCV 2015 code arxiv supplement | |
Learning to propose objects Philipp Krähenbühl, Vladlen Koltun CVPR 2015 code pdf | |
2014 | |
Geodesic Object Proposals Philipp Krähenbühl, Vladlen Koltun ECCV 2014 code pdf | |
2013 | |
Parameter Learning and Convergent Inference for Dense Random Fields Philipp Krähenbühl, Vladlen Koltun ICML 2013 code pdf project | |
2012 | |
Efficient Nonlocal regularization for Optical Flow Philipp Krähenbühl, Vladlen Koltun ECCV 2012 | |
Saliency Filters: Contrast Based Filtering for Salient Region Detection Federico Perazzi, Philipp Krähenbühl, Yael Pritch, Alexander Hornung CVPR 2012 code pdf project | |
2011 | |
Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials Philipp Krähenbühl, Vladlen Koltun NIPS 2011 code arxiv project | |
2010 | |
Gesture Controllers Sergey Levine, Philipp Krähenbühl, Sebastian Thrun, Vladlen Koltun SIGGRAPH 2010 | |
2009 | |
A system for retargeting of streaming video Philipp Krähenbühl, Manuel Lang, Alexander Hornung, Markus Gross SIGGRAPH Asia 2009 |
Research group
PhD Students:
Past PhD students:
- Dian Chen (2023, next: Waabi)
- Xingyi Zhou (2022, next: Google)
- Chao-Yuan Wu (2021, next: Facebook)
Undergraduates and MS:
Past undergraduates and MS:
- Tianwei Yin (next: MIT)
- Scott Cao (next: Facebook)
- David Wang (next: Amazon)
- Chia-Wen Cheng (next: Facebook)
- Mina Lee (next: Google)
- Kamil Ali (next: Stanford)
- Brady Zhou (next: Intel, then UT)
- Nayan Singhal (next: Facebook AML)
- Shaayaan Sayed (next: some hedgefund)
- Nimit Kalra (next: finance)
Teaching
- CS342 - Neural networks - Fall 2017, 2018, 2019
- CS395T - Deep learning seminar - Fall 2016, 2017, 2018, 2019
- CS394D - Deep learning WB - all year 2019-
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
.