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New for: D1, D2, D3, INET, D4, D5, D6

What and Who

All-Season Semantic Scene Understanding for Autonomous Driving

Dengxin Dai
MMCI
Joint Lecture Series
AG 1, AG 2, AG 3, INET, AG 4, AG 5, D6, SWS, RG1, MMCI  
Public Audience
English

Date, Time and Location

Wednesday, 6 October 2021
12:15
60 Minutes
Virtual talk
Virtual
Saarbrücken

Abstract

While steady progress has been made in visual perception, the performance is mainly benchmarked under fair weather/lighting conditions. Even the best-performing algorithms on the existing benchmarks can become untrustworthy in unseen domains or in adverse weather/lighting conditions. The ability to robustly cope with those conditions is absolutely essential for outdoor applications such as autonomous driving. In this talk, I will present our work on semantic scene understanding under adverse weather/illumination conditions and under general unseen domains. This covers multiple contributions: weather phenomenon simulation, curriculum domain adaptation, reference-guided learning, supervision fusion, sensor fusion, and supervision distillation. Our methods all contribute towards the goal of all-season perception and have achieved state-of-the-art performance for semantic scene understanding under bad weather/lighting conditions and under the synthetic2real cross-domain setting.

Contact

Jennifer Müller
+49 681 9325 2900
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Virtual Meeting Details

Zoom
997 1565 5535
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Jennifer Müller, 11/25/2021 11:26
Jennifer Müller, 09/28/2021 11:48 -- Created document.