Highlights
11 papers (including 3 Orals) have been accepted to CVPR 2022
Congratulations to all the amazing students and collaborators!
- Lukas Hoyer, Dengxin Dai, Luc Van Gool. “DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation“, Code
- Ozan Unal, Dengxin Dai, Luc Van Gool. “Scribble-Supervised LiDAR Semantic Segmentation“, Code (Oral)
- Yue Fan, Dengxin Dai, Bernt Schiele. […]
Dengxin Dai receives the Golden Owl Award
We are very proud to announce that Dr. Dengxin Dai has received the Golden Owl Award 2021 for exceptional teaching.
The Golden Owl honours excellent teachers. The Owl is awarded by VSETH, ETH Zurich’s student association.
All ETH members with a teaching assignment can be nominated for the Golden Owl. One lecturer […]
News
13/05/2022:
Dr. Dengxin Dai has joined the IJCV Editorial Board as Editor.
13/05/2022:
Lukas Hoyer has been honoured the ETH Outstanding Master’s Theses Award under Dr. Dengxin Dai’s supervision. Congratulations!
11/03/2022:
11 papers (3 Orals) accepted to CVPR 2022. Congratulations to all co-authors!
11/03/2022:
VAS has established new research projects with Toyota!
11/03/2022:
Dr. Dai is Area Chair of ECCV 2022.
11/03/2022:
Our work Binaural Soundnet has been accepted to TPAMI as is!
17/01/2022:
Our CVPR’22 Vision for All Seasons workshop has been accepted with excellent lineup of speakers, four challenges and CFP.
16/01/2022:
Dengxin Dai has received an academic gift funding from Facebook Reality Lab.
16/01/2022:
Four papers have been accepted to RAL: 3D LiDAR Semantic Segmentation, End2End LiDAR Beam Optimisation, 3D MOT, and Depth Estimation with HD Map Prior.
31/10/2021:
We are hiring PhDs and PostDocs!
Scientific Mission
The scientific mission of VAS is to develop robust and scalable perception systems for real-world applications. We focus on deep learning-based perception for autonomous systems such as autonomous driving. We are especially fascinated about scaling existing visual perception models to novel domains (e.g. adverse weather/lighting conditions, low-quality data), to more data modality (e.g. LiDAR, Radar, Events, Audio, HD Maps), to unseen classes (e.g. rare classes), and to new tasks. The relevant research topics are summarised in the diagram shown on the right.

Publications
This work develops an approach for scene understanding purely based on binaural sounds.
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
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 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.