Joint Monocular 3d Vehicle Detection and Tracking Github

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

2021
	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:

• Xingyi Zhou
• Dian Chen
• Brady Zhou

Past PhD students:

• Chao-Yuan Wu (FAIR)

Undergraduates and MS:

• Nimit Kalra
• Tianwei Yin

Past undergraduates and MS:

• 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)

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:

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If all the above fail, just use Kraehenbuehl.

Joint Monocular 3d Vehicle Detection and Tracking Github

Source: http://www.philkr.net/

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