
Dengxin Dai
Senior Researcher
E-Mail: ddai [at] mpi-inf.mpg.de
Phone: +49 681 9325 2104
Address: MPI for Informatics, Saarland Informatics Campus, 66123 Saarbrücken
Room: E1 4 – 604
We present Scale-aware Domain Adaptive Faster R-CNN, a model aiming at improving the cross-domain robustness of object detection.
We propose a projection-based method for semantic segmentation of LiDar data, called Multi-scale Interaction Network (MINet), which is very efficient and accurate.
This work develops an approach for scene understanding purely based on binaural sounds.
We present a domain flow generation (DLOW) model to bridge two different domains by generating a continuous sequence of intermediate domains flowing from one domain to the other.
We introduce Task Switching Networks (TSNs), a task-conditioned architecture with a single unified encoder/decoder for efficient multi-task learning.
A novel UDA method, DAFormer, consisting of a Transformer encoder and a multi-level context-aware feature fusion decoder, improve SOTA by 10.8 mIoU for GTA->Cityscapes and 5.4 mIoU for Synthia->Cityscapes
Preprint
We propose a novel co-learning framework (CoSSL) with decoupled representation learning and classifier learning for imbalanced SSL.
Preprint
Zero-shot semantic segmentation (ZS3) aims to segment the novel categories that have not been seen in the training. We propose to decouple the ZS3 into two sub-tasks: 1) a class-agnostic grouping task to group the pixels into segments. 2) a zero-shot classification task on segments.
This work presents a novel method for LiDAR-based 3D object detection in foggy weather by simulating foggy effects into standard LiDAR data..
ACDC is a large-scale dataset for training and testing semantic segmentation methods for four adverse visual conditions: fog, nighttime, rain, and snow.
A novel method to leverage the guidance from self-supervised depth estimation to bridge the domain gap for semantic segmentation, achieving state-of-the-art performance..
A novel manner to learn end-to-end driving from a reinforcement learning expert that maps bird's-eye view images to continuous low-level actions, achieving the state-of-the-art perfomrance.
A novel method to combine and reuse existing datasets that belong to different domains, have partial annotations, and/or have different data modalities, to boost the overall performance on the target domain.
We propose a novel end-to-end driving method that can learn how-to-drive directly from surrounding-view videos and route planners.
- Area Chair of WACV 2020, CVPR 2021, CVPR 2022, ECCV 2020.
- Co-organizer of Workshop DeepMTL: Deep Multi-Task Learning at ICCV’21.
- Lead Guest Editor of the Special Issue “Computer Vision for All Seasons” of IJCV.
- Senior Program Committee Member: AAAI 2020 and IJCAI 2019.
- Lead workshop organizer: Vision for All Seasons at CVPR’19, CVPR’20, and CVPR’21.
- Lead workshop organizer: Autonomous Driving workshop at ICCV’19.
- Co-organizer of challenge Learning to Drive at ICCV’19
- Co-organizer of challenge Nighttime Semantic Image Segmentation at CVPR’20.
- Jury member for the Pioneer Fellowship Program at ETH Zurich.
- Examiner of Doctoral Exams: EPFL, NUS and ETH Zurich.
- Regular Reviewers: CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, ICRA, IROS, AAAI, IJCV, and PAMI.
- The IV workshop “Beyond supervised learning: addressing data scarcity in intelligent transportation systems“, 2022
- The “Learning to Understand Aerial Images (LUAI)” Workshop at ICCV, Oct. 2021.
- The Saarland Informatics Campus Lecture Series, Oct. 2021.
- The “Radar Perception for All-Weather Autonomy” Workshop at ICRA, May 2021.
- Zurich High School, “General Introduction to Autonomous Driving”, Nov 2020.
- The Robotics and Perception Group at the University of Zurich, Sep. 2020.
- The “Transferring and Adapting Source Knowledge in ComputerVision and VISDA Challenge” Workshop at ECCV, 2020.
- The “Map-based Localization for Autonomous Driving” Workshop at ECCV, 2020.
- The “Commands for Autonomous Vehicle” Workshop at ECCV, 2020.
- The “Autonomous Driving Workshop” at CVPR, 2020.
- The “Bridging the Gap between Computational Photography and Visual Recognition” Workshop at CVPR, 2020.
- The “Physics-Based Vision meets Deep Learning (PBDL)” Workshop at ICCV, 2019.
-
International VDI Conference – Future of AI in Automotive, Berlin, 2018.
-
The “ApolloScape: the Vision-based Navigation for Autonomous Driving” Workshop at ECCV, 2018.
- Scientifica: Zurich Science Days, 2017.
-
Lead Lecturer, Deep Learning for Autonomous Driving, 6 ECTS, ETH Zurich, 2020 (80+ students).
- Lead Lecturer, Deep Learning for Autonomous Driving, 6 ECTS, ETH Zurich, 2021 (100+ students).
- Lead Lecturer, Deep Learning for Autonomous Driving, 6 ECTS, ETH Zurich, 2022 (100+ students).
- Tutorial “From Image Restoration to Enhancement and Beyond” at ICCV, 2019.