Search Results for author: Michael Hofmann

Found 7 papers, 1 papers with code

Find it if You Can: End-to-End Adversarial Erasing for Weakly-Supervised Semantic Segmentation

1 code implementation9 Nov 2020 Erik Stammes, Tom F. H. Runia, Michael Hofmann, Mohsen Ghafoorian

Semantic segmentation is a task that traditionally requires a large dataset of pixel-level ground truth labels, which is time-consuming and expensive to obtain.

Weakly-Supervised Semantic Segmentation

VATLD: A Visual Analytics System to Assess, Understand and Improve Traffic Light Detection

no code implementations27 Sep 2020 Liang Gou, Lincan Zou, Nanxiang Li, Michael Hofmann, Arvind Kumar Shekar, Axel Wendt, Liu Ren

In this work, we propose a visual analytics system, VATLD, equipped with a disentangled representation learning and semantic adversarial learning, to assess, understand, and improve the accuracy and robustness of traffic light detectors in autonomous driving applications.

Autonomous Driving Decision Making +1

I Bet You Are Wrong: Gambling Adversarial Networks for Structured Semantic Segmentation

no code implementations7 Aug 2019 Laurens Samson, Nanne van Noord, Olaf Booij, Michael Hofmann, Efstratios Gavves, Mohsen Ghafoorian

Adversarial training has been recently employed for realizing structured semantic segmentation, in which the aim is to preserve higher-level scene structural consistencies in dense predictions.

Semantic Segmentation

Dynamic Adaptation on Non-Stationary Visual Domains

no code implementations2 Aug 2018 Sindi Shkodrani, Michael Hofmann, Efstratios Gavves

To demonstrate the effectiveness of our proposed framework, we modify associative domain adaptation to work well on source and target data batches with unequal class distributions.

Domain Adaptation Semantic Segmentation

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