Dengxin Dai


Welcome

Dengxin is a Lecturer working with the ​​Computer Vision Lab at ETH Zurich. He is leading the research group TRACE-Zurich working on Autonomous Driving within the R&D project "TRACE: Toyota Research on Automated Cars in Europe". In 2016, he obtained his PhD at ETH Zurich under the supervision of Prof. Luc Van Gool and Prof. Gerhard Schmitt. During the PhD study, he was working on the project VarCity for City Modeling based on camera data.

He has organized the CVPR'19 workshop Vision for All Seasons: Bad Weather and Nighttime, and the ICCV'19 workshop Autonomous Driving. He is Guest Editor for the IJCV special issue "Vision for All Seasons", was an Area Chair for WACV 2020 and will be an Area Chair for CVPR 2021. He has been a program committee member of several major computer vision conferences and received multiple outstanding reviewer awards.

His current research interests include (1) Robust Road Scene Understanding; (2) Learning Driving Models; (3) Human-Vehicle Communication; (4) Sensor Fusion; and (5) Multi-Task Learning.

News


10-12-19
Call for papers: IJCV Special Issue “Computer Vision for All Seasons”. Deadline is extended to 15 March 2020. Check out here for more details.

30-01-19
Our CVPR2020 workshop Vision for All Seasons: Bad Weather and Nighttime has been accepted! Stay tuned for call-for-papers and challenges.


20-04-19
Our workshop on Autonomous Driving will be held in conjunction with ICCV 2019. Check out for paper submission and participation in the learning-to-drive (L2D) challenge!


30-01-19
Our workshop Vision for All Seasons: Bad Weather and Nighttime will be held in conjunction with CVPR 2019. Check out for paper submission and participation!


11-12-18
I have given a talk about our work "Going Door-to-Door with End-to-End Training" at International VDI Conference - Future of AI in Automotive.


09-09-18
I have given a talk about our work "Robust Visual Perception" and "End-to-End Driving" at ApolloScape: Vision-based Navigation for Autonomous Driving workshop, held in conjunction with ECCV 2018.

Publications

2020

"Revisiting Multi-Task Learning in the Deep Learning Era", Simon Vandenhende, Stamatios Georgoulis, Marc Proesmans, Dengxin Dai and Luc Van Gool, 2020.
[arXiv]

"Semantic Object Prediction and Spatial Sound Super-resolution with Binaural Sounds", Arun Balajee Vasudevan, Dengxin Dai and Luc Van Gool, 2020.
[arXiv] [BibTex] [Project]
A novel method for semantic object prediciton, depth estimation and spatial sound super-resolution with binaural sounds.

"Don’t Forget The Past: Recurrent Depth Estimation from Monocular Video", Vaishakh Patil, Wouter Van Gansbeke, Dengxin Dai and Luc Van Gool, 2020.
[arXiv] [BibTex]
A novel method to produce a time series of depth maps by exploiting the spatiotemporal structures of depth across frames.

"Talk2Nav: Long-Range Vision-and-Language Navigation with Dual Attention and Spatial Memory", Arun Balajee Vasudevan, Dengxin Dai and Luc Van Gool, 2020.
[arXiv] [BibTex] [Project] [Video]
A novel method for long-range robot navigation in cities with language instructions.

"Action Sequence Predictions of Vehicles in Urban Environments using Map and Social Context", Jan-Nico Zaech, Dengxin Dai, Alex Liniger, and Luc Van Gool, 2020.
[arXiv]

2019

"Learning Accurate, Comfortable and Human-like Driving", Simon Hecker, Dengxin Dai and Luc Van Gool, 2019.
[arXiv] [BibTex] [Project] [Data] [Workshop]
A novel driving model which is more accurate, more comfortable and behaves more like a human driver than previous methods.

"Guided Curriculum Model Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation", Christos Sakaridis , Dengxin Dai and Luc Van Gool, ICCV 2019.
[arXiv] [BibTex] [Project]
A new method for learning, data annotation, method evaluation and a new dataset for semantic nighttime image segmentation.

"Semantic Understanding of Foggy Scenes with Purely Synthetic Data", Martin Hahner, Dengxin Dai, Christos Sakaridis, Jan-Nico Zaech , Luc Van Gool, ITSC 2019.
[PDF] [BibTex] [Project] [Code]

"Learning a Curve Guardian for Motorcycles", Simon Hecker, Alex Liniger, Henrik Maurenbrecher, Dengxin Dai and Luc Van Gool, ITSC 2019.
[Paper] [BibTex] [Venturebeat] [MOTO.IT] [Rideapart] ...
A new road curvature warning system for motorcycles, combining advances in computer vision, optimal control and mapping.

"Texture Underfitting for Domain Adaptation", Jan-Nico Zaech, Martin Hahner, Dengxin Dai, Luc Van Gool, ITSC 2019.
[PDF] [BibTex] [Project]

"Real-time 3D Traffic Cone Detection for Autonomous Driving",
Ankit Dhall , Dengxin Dai and Luc Van Gool, IEEE Intelligent Vehicles Symposium (IV), 2019.
[Paper] [BibTex] [Video] [Project]
A new method for real-time 3D traffic cone detection, deployed in a real racing car (see our video demo)

"Curriculum Model Adaptation with Synthetic and Real Data for Semantic Foggy Scene Understanding", Dengxin Dai, Christos Sakaridis, Simon Hecker and Luc Van Gool, International Journal of Computer Vision (IJCV), 2019.
[Paper] [Arxiv] [BibTex] [Project] [Code] [Data]
A novel curriculum model adaptation method to transfer semantic models from clear-weather condition to foggy weather condition by combining synthetic and real data. Synthetic and real fog datasets are provided.

2018

"End-to-End Learning of Driving Models with Surround-View Cameras and Route Planners", Simon Hecker, Dengxin Dai, Luc Van Gool, ECCV 2018.
[PDF] [Poster] [arXiv] [BibTex] [Demo Video] [Project]

"Failure Prediction for Autonomous Driving", Simon Hecker, Dengxin Dai, Luc Van Gool, IEEE Intelligent Vehicles Symposium, 2018.
[PDF] [BibTex]

"Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding", Christos Sakaridis, Dengxin Dai, Simon Hecker, Luc Van Gool, ECCV 2018.
[arXiv] [PDF] [BibTex] [Project] [Code] [Data]

"Semantic Foggy Scene Understanding with Synthetic Data", Christos Sakaridis, Dengxin Dai, Luc Van Gool, International Journal of Computer Vision, 2018.
[PDF (preprint)] [PDF (published)] [BibTex] [Project] [Data] [Code]

"Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime", Dengxin Dai, Luc Van Gool, International Conference on Intelligent Transportation Systems (ITSC), 2018.
[PDF] [BibTex] [Project] [Data]

"Domain Adaptive Faster R-CNN for Object Detection in the Wild", Yuhua Chen, Wen Li, Christos Sakaridis, Dengxin Dai, Luc Van Gool CVPR 2018.
[PDF] [BibTex] [Code]

"Object Referring in Videos with Language and Human Gaze", Arun Balajee Vasudevan, Dengxin Dai, Luc Van Gool. CVPR 2018.
[PDF] [BibTex] [Demo Video ] [Project]

"Object Referring in Visual Scene with Spoken Language", Arun Balajee Vasudevan, Dengxin Dai, Luc Van Gool, WACV 2018.
[PDF] [BibTex] [Demo Video ] [Project]

2017

"PathTrack: Fast Trajectory Annotation with Path Supervision", S. Manen, M. Gygli, D. Dai, L. Van Gool, ICCV 2017.
[paper] [BibTex] [Data] [Demo Video] [Project]

"Deep Domain Adaptation by Geodesic Distance Minimization", Yifei Wang, Wen Li, Dengxin Dai, Luc Van Gool, ICCV Workshop, 2017.
[paper] [BibTex]

"VarCity - The Video: the Struggles and Triumphs of Leveraging Fundamental Research Results in a Graphics Video Production", K. Vanhoey, C. Porto de Oliveira, H. Riemenschneider, A. Bodis-Szomoru, S. Manen, D. Paudel, M. Gygli, N. Kobyshev, T. Kroeger, D. Dai, L. Van Gool,, Siggraph Talk 2017.
[paper] [BibTex] [Video] [Project]

"Domain Generalization and Adaptation using Low Rank Exemplar SVMs", W. Li, Z. Xu, D. Xu, D. Dai, and L. Van Gool , T-PAMI.
[paper] [BibTex]

"Speech-based Visual Question Answering", Ted Zhang, Dengxin Dai, Tinne Tuytelaars, Marie-Francine Moens, Luc Van Gool
[paper] [BibTex] [Code] [Data]

2016

"Fast Optical Flow using Dense Inverse Search", T. Kroeger, R. Timofte, D. Dai, and L. Van Gool , ECCV 2016.
[paper] [project] [code] [BibTex]

"Scale-Aware Alignment of Hierarchical Image Segmentation", Y. Chen, D. Dai, J. Pont-Tuset and L. Van Gool , CVPR 2016.
[paper] [code] [BibTex]

"Fast Algorithms for Linear and Kernel SVM+", W. Li, D. Dai, M. Tan, D. Xu and L. Van Gool , CVPR 2016.
[paper] [code ] [BibTex]

"Is Image Suepr-resolution Helpful for Other Vision Tasks?", D. Dai, Y. Wang, Y. Chen, L. Van Gool, WACV 2016.
[paper] [ arXiv link] [project ] [ code ] [BibTex]

"Leveraging single for multi-target tracking using a novel trajectory overlap affinity measure", S. Manen, R. Timofte, D. Dai, L. Van Gool, WACV 2016.
[paper] [Supp. Material] [BibTex]

"Unsupervised High-level Feature Learning by Ensemble Projection for Semi-supervised Image Classification and Image Clustering", Dengxin Dai and Luc Van Gool, Tech. Report.
[paper]     [project]     [code]     [BibTex]    

2015

"Metric imitation by manifold transfer for efficient vision applications", D. Dai, T. Kroeger, R. Timofte, L. Van Gool, CVPR 2015.
[paper] [poster] [supp. mat.] [abstract] [project] [code] [BibTex]

"Joint Vanishing Point Extraction and Tracking", T. Kroeger, D. Dai, L. Van Gool, CVPR 2015, (Oral).
[paper] [supp. mat.] [abstract] [project] [BibTex]

"Jointly Optimized Regressors for Image Super-resolution", D. Dai, R. Timofte, L. Van Gool, Eurographics 2015, (Oral).
[paper] [project] [code] [slide] [BibTex]

"Discovery of Sets of Mutually Orthogonal Vanishing Points in Videos", T. Kroeger, D. Dai, R. Timofte, L. Van Gool, WACV-W 2015.
[paper] [BibTex]

2014

"The Synthesizability of Texture Examples", Dengxin Dai, Hayko Riemenschneider, and Luc Van Gool, CVPR 2014.
[project] [code] [data] [paper] [BibTex]

"Latent Dictionary Learning for Sparse Representation based Classification", Meng Yang, Dengxin Dai, Linlin Shen, and Luc Van Gool, CVPR 2014.
[paper] [BibTex]

2013

"Example-based Facade Texture Synthesis", Dengxin Dai, Hayko Riemenschneider, Gerhard Schmitt, and Luc Van Gool, ICCV 2013 .
[project]     [data]     [PDF]     [BibTex]    

"Ensemble Projection for Semi-supervised Image Classification", Dengxin Dai and Luc Van Gool, ICCV 2013 .
[project]     [code]     [PDF]     [BibTex]    

2012

"Learning Domain Knowledge for Facade Labeling", Dengxin Dai, Mukta Prasad, Gerhard Schmitt, and Luc Van Gool, ECCV 2012 .
[project]     [PDF]     [BibTex]    

"Ensemble Partitioning for Unsupervised Image Categorization", Dengxin Dai, Mukta Prasad, Christian Leistner, and Luc Van Gool, ECCV 2012 .
[project]     [PDF]     [BibTex]    

"SAR-Based Terrain Classification Using Weakly Supervised Hierarchical Markov Aspect Models", Wen Yang, Dengxin Dai, Bill Triggs, and Gui-Song Xia, IEEE Trans. Image Process , 2012.
[PDF]     [BibTex]    

Previous

"Multilevel Local Pattern Histogram for SAR Image Classification", Dengxin Dai, Wen Yang, and Hong Sun, IEEE Geosci. Remote Sens. Lett. , 2011.
[code]     [PDF]     [BibTex]    

"Satellite Image Classification via Two-Layer Sparse Coding With Biased Image Representation ", Dengxin Dai and Wen Yang IEEE Geosci. Remote Sens. Lett. , 2011.
[data]     [PDF]     [BibTex]    

"Discovering Scene Categories by Information Projection and Cluster Sampling", Dengxin Dai, Tianfu Wu, and Song-Chun Zhu CVPR 2010
[PDF]     [BibTex]    

Code

  • The code of some of my work can be found at the corresponding project page!
  • Bing Image Crawler : A Shell + Python script to download images from Bing image search!
  • A matlab wrapper (linux) for the Ranking SVM !
  • Services

  • Conference Reviewer: NIPS 2018, CVPR 2018, ITSC 2018, CVPR 2017, SIGGRAPH 2016, CVPR 2016, Pacific Graphics 2016, CVPR 2015, SIGGRAPH 2014.
  • Journal Reviewer: IEEE Trans. Pattern Anal. Mach. Intell., International Journal of Computer Vision, IEEE Trans. Image Process. , IEEE Trans. Intelligent Transportation Systems. , IEEE Trans. Geosci. Remote Sens. , IEEE Transactions on Multimedia, Comput. Vis. and Image Und. , Pattern Recognition and IEEE Geosci. Remote Sens. Lett.
  • Contact Me

    Computer Vision Laboratory
    Sternwartstrasse 7
    ETH Zentrum
    CH - 8092 Zürich, Switzerland
    Tel: +41 44 63 25426
    Fax: +41 44 63 21199
    Office: ETF C112
    E-mail: dai[at]vision.ee.ethz.ch